TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NAType'

**Describe the issue**: `to_pandas()` crashes when encountering NA values in an INT64 OPTIONAL column (_cycle_ in this parquet file)

**Minimal Complete Verifiable Example**:

```python
import fastparquet as fp

pf = fp.ParquetFile("ffg_partial.parquet")
pf.to_pandas()
```

<details>
  <summary><code>TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NAType'</code></summary>

```
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[71], [line 1](vscode-notebook-cell:?execution_count=71&line=1)
----> [1](vscode-notebook-cell:?execution_count=71&line=1) df = pf.to_pandas()

File /projects/europa/users/brechtm/clean/.venv/lib/python3.12/site-packages/fastparquet/api.py:790, in ParquetFile.to_pandas(self, columns, categories, filters, index, row_filter, dtypes)
    [786](.../fastparquet/api.py:786)         continue
    [787](.../fastparquet/api.py:787)     parts = {name: (v if name.endswith('-catdef')
    [788](.../fastparquet/api.py:788)                     else v[start:start + thislen])
    [789](.../fastparquet/api.py:789)              for (name, v) in views.items()}
--> [790](.../fastparquet/api.py:790)     self.read_row_group_file(rg, columns, categories, index,
    [791](.../fastparquet/api.py:791)                              assign=parts, partition_meta=self.partition_meta,
    [792](.../fastparquet/api.py:792)                              row_filter=sel, infile=infile)
    [793](.../fastparquet/api.py:793)     start += thislen
    [794](.../fastparquet/api.py:794) return df

File /projects/europa/users/brechtm/clean/.venv/lib/python3.12/site-packages/fastparquet/api.py:388, in ParquetFile.read_row_group_file(self, rg, columns, categories, index, assign, partition_meta, row_filter, infile)
    [385](.../fastparquet/api.py:385)     ret = True
    [386](.../fastparquet/api.py:386) f = infile or self.open(fn, mode='rb')
--> [388](.../fastparquet/api.py:388) core.read_row_group(
    [389](.../fastparquet/api.py:389)     f, rg, columns, categories, self.schema, self.cats,
    [390](.../fastparquet/api.py:390)     selfmade=self.selfmade, index=index,
    [391](.../fastparquet/api.py:391)     assign=assign, scheme=self.file_scheme, partition_meta=partition_meta,
    [392](.../fastparquet/api.py:392)     row_filter=row_filter
    [393](.../fastparquet/api.py:393) )
    [394](.../fastparquet/api.py:394) if ret:
    [395](.../fastparquet/api.py:395)     return df

File /projects/europa/users/brechtm/clean/.venv/lib/python3.12/site-packages/fastparquet/core.py:644, in read_row_group(file, rg, columns, categories, schema_helper, cats, selfmade, index, assign, scheme, partition_meta, row_filter)
    [642](.../fastparquet/core.py:642) if assign is None:
    [643](.../fastparquet/core.py:643)     raise RuntimeError('Going with pre-allocation!')
--> [644](.../fastparquet/core.py:644) read_row_group_arrays(file, rg, columns, categories, schema_helper,
    [645](.../fastparquet/core.py:645)                       cats, selfmade, assign=assign, row_filter=row_filter)
    [647](.../fastparquet/core.py:647) for cat in cats:
    [648](.../fastparquet/core.py:648)     if cat not in assign:
    [649](.../fastparquet/core.py:649)         # do no need to have partition columns in output

File /projects/europa/users/brechtm/clean/.venv/lib/python3.12/site-packages/fastparquet/core.py:615, in read_row_group_arrays(file, rg, columns, categories, schema_helper, cats, selfmade, assign, row_filter)
    [612](.../fastparquet/core.py:612)     continue
    [613](.../fastparquet/core.py:613) remains.discard(name)
--> [615](.../fastparquet/core.py:615) read_col(column, schema_helper, file, use_cat=name+'-catdef' in out,
    [616](.../fastparquet/core.py:616)          selfmade=selfmade, assign=out[name],
    [617](.../fastparquet/core.py:617)          catdef=out.get(name+'-catdef', None),
    [618](.../fastparquet/core.py:618)          row_filter=row_filter)
    [620](.../fastparquet/core.py:620) if _is_map_like(schema_helper, column):
    [621](.../fastparquet/core.py:621)     # TODO: could be done in fast loop in _assemble_objects?
    [622](.../fastparquet/core.py:622)     if name not in maps:

File /projects/europa/users/brechtm/clean/.venv/lib/python3.12/site-packages/fastparquet/core.py:561, in read_col(column, schema_helper, infile, use_cat, selfmade, assign, catdef, row_filter)
    [559](.../fastparquet/core.py:559)     part = part._data
    [560](.../fastparquet/core.py:560) elif part.dtype.kind != "O":
--> [561](.../fastparquet/core.py:561)     part[defi != max_defi] = my_nan
    [562](.../fastparquet/core.py:562) if d and not use_cat:
    [563](.../fastparquet/core.py:563)     part[defi == max_defi] = dic[val]

TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NAType'
```
</details>

I cannot share the parquet file, so hopefully the following information is sufficient to debug the issue.

```
> pf.selfmade
True
```

```
> pf.dtypes
OrderedDict([('net', 'category'),
             ('cycle', dtype('int64')),
             ('edge', 'category'),
             ('time20', dtype('float64')),
             ('time50', dtype('float64')),
             ('time80', dtype('float64')),
             ('global_variation', 'category'),
             ('sweep_index', 'category'),
             ('temp', 'category'),
             ('vdd', 'category'),
             ('vdd_imc', 'category'),
             ('vss', 'category')])
```

```
> print(pf.schema)
- schema: 
| - net: BYTE_ARRAY, UTF8, OPTIONAL
| - cycle: INT64, OPTIONAL
| - edge: BYTE_ARRAY, UTF8, OPTIONAL
| - time20: DOUBLE, OPTIONAL
| - time50: DOUBLE, OPTIONAL
| - time80: DOUBLE, OPTIONAL
| - global_variation: BYTE_ARRAY, UTF8, OPTIONAL
| - sweep_index: INT64, OPTIONAL
| - temp: INT64, OPTIONAL
| - vdd: DOUBLE, OPTIONAL
| - vdd_imc: DOUBLE, OPTIONAL
  - vss: INT64, OPTIONAL
```

<details>
  <summary><code>pf.statistics</code></summary>

```
{'min': {'net': [None],
         'cycle': [2, 0, 2, 0],
         'edge': [None],
         'time20': [1.4773426876688636e-10,
                    1.0559080883552939e-10,
                    7.139149389929003e-11,
                    9.52384645968221e-11],
         'time50': [1.5455092029759065e-10,
                    1.3213713977167085e-19,
                    1.672420481250059e-10,
                    1.3213713977167085e-19],
         'time80': [1.473934018947706e-10,
                    3.0000002099352238e-12,
                    1.3538265149883599e-10,
                    3.0000002099352238e-12],
         'global_variation': [None],
         'sweep_index': [None, None, None, None],
         'temp': [None, None, None, None],
         'vdd': [None, None, None, None],
         'vdd_imc': [None, None, None, None],
         'vss': [None, None, None, None]},
 'max': {'net': [None],
         'cycle': [1024, 1033, 1024, 1033],
         'edge': [None],
         'time20': [6.524647908841792e-10,
                    9.998369878836728e-09,
                    6.761692109951692e-10,
                    9.99867577012857e-09],
         'time50': [6.316129033313601e-10,
                    6.376947296995981e-10,
                    6.755620766233663e-10,
                    6.880282769349157e-10],
         'time80': [6.419314096761683e-10,
                    9.11409428754411e-09,
                    6.881378599573002e-10,
                    6.971354202572327e-10],
         'global_variation': [None],
         'sweep_index': [None, None, None, None],
         'temp': [None, None, None, None],
         'vdd': [None, None, None, None],
         'vdd_imc': [None, None, None, None],
         'vss': [None, None, None, None]},
 'null_count': {'net': [0, 0, 0, 0],
                'cycle': [0, 7, 0, 7],
                'edge': [0, 0, 0, 0],
                'time20': [3540142, 773119, 2661380, 2569291],
                'time50': [5093064, 1212040, 4006276, 3655088],
                'time80': [6684602, 1666286, 5130506, 4509842],
                'global_variation': [0, 0, 0, 0],
                'sweep_index': [0, 0, 0, 0],
                'temp': [0, 0, 0, 0],
                'vdd': [0, 0, 0, 0],
                'vdd_imc': [0, 0, 0, 0],
                'vss': [0, 0, 0, 0]},
 'distinct_count': {'net': [None, None, None, None],
                    'cycle': [None, None, None, None],
                    'edge': [None, None, None, None],
                    'time20': [None, None, None, None],
                    'time50': [None, None, None, None],
                    'time80': [None, None, None, None],
                    'global_variation': [None, None, None, None],
                    'sweep_index': [None, None, None, None],
                    'temp': [None, None, None, None],
                    'vdd': [None, None, None, None],
                    'vdd_imc': [None, None, None, None],
                    'vss': [None, None, None, None]}}
```
</details>

<details>
  <summary><code>pf.row_groups</code></summary>

```
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       value: b'int32'
     - key: b'label_dtype'
       value: b'object'
     num_values: 10127116
     path_in_schema:
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     statistics:
       distinct_count: null
       max: null
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       max: b'\x00\x04\x00\x00\x00\x00\x00\x00'
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       min: b'\x02\x00\x00\x00\x00\x00\x00\x00'
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     path_in_schema:
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     num_values: 10127116
     path_in_schema:
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       max: null
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       value: b'object'
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     statistics:
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```
</details>

<details>
  <summary><code>pf.pandas_metadata</code></summary>

```
{'column_indexes': [{'field_name': None,
                     'metadata': None,
                     'name': None,
                     'numpy_type': 'object',
                     'pandas_type': 'mixed-integer'}],
 'columns': [{'field_name': 'net',
              'metadata': {'num_categories': 1307603, 'ordered': False},
              'name': 'net',
              'numpy_type': 'int32',
              'pandas_type': 'categorical'},
             {'field_name': 'cycle',
              'metadata': None,
              'name': 'cycle',
              'numpy_type': 'int64',
              'pandas_type': 'int64'},
             {'field_name': 'edge',
              'metadata': {'num_categories': 2, 'ordered': False},
              'name': 'edge',
              'numpy_type': 'int8',
              'pandas_type': 'categorical'},
             {'field_name': 'time20',
              'metadata': None,
              'name': 'time20',
              'numpy_type': 'float64',
              'pandas_type': 'float64'},
             {'field_name': 'time50',
              'metadata': None,
              'name': 'time50',
              'numpy_type': 'float64',
              'pandas_type': 'float64'},
             {'field_name': 'time80',
              'metadata': None,
              'name': 'time80',
              'numpy_type': 'float64',
              'pandas_type': 'float64'},
             {'field_name': 'global_variation',
              'metadata': {'num_categories': 1, 'ordered': False},
              'name': 'global_variation',
              'numpy_type': 'int8',
              'pandas_type': 'categorical'},
             {'field_name': 'sweep_index',
              'metadata': {'num_categories': 2, 'ordered': False},
              'name': 'sweep_index',
              'numpy_type': 'int8',
              'pandas_type': 'categorical'},
             {'field_name': 'temp',
              'metadata': {'num_categories': 2, 'ordered': False},
              'name': 'temp',
              'numpy_type': 'int8',
              'pandas_type': 'categorical'},
             {'field_name': 'vdd',
              'metadata': {'num_categories': 1, 'ordered': False},
              'name': 'vdd',
              'numpy_type': 'int8',
              'pandas_type': 'categorical'},
             {'field_name': 'vdd_imc',
              'metadata': {'num_categories': 1, 'ordered': False},
              'name': 'vdd_imc',
              'numpy_type': 'int8',
              'pandas_type': 'categorical'},
             {'field_name': 'vss',
              'metadata': {'num_categories': 1, 'ordered': False},
              'name': 'vss',
              'numpy_type': 'int8',
              'pandas_type': 'categorical'}],
 'creator': {'library': 'fastparquet', 'version': '2024.11.0'},
 'index_columns': [{'kind': 'range',
                    'name': None,
                    'start': 0,
                    'step': 1,
                    'stop': 10127116}],
 'pandas_version': '2.2.3',
 'partition_columns': []}
```
</details>

**Anything else we need to know?**: I can read this parquet file using pyarrow (pandas.read_parquet)), which shows 14 rows were cycle is NaN. Of course, this loses all category information.

Possibly related:
- #808 
- #901 
- #948 

**Environment**:

- Dask version: fastparquet 2024.11.0
- Python version: Python 3.12.0
- Operating System: Red Hat Enterprise Linux Workstation release 7.4 (Maipo)
- Install method (conda, pip, source): pip

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TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NAType' #959

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Uh oh!

TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NAType' #959

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