[NVBUG 5801937] Disable dq_only by default (#777)

## What does this PR do?

**Type of change:** 
Bug fix

**Overview:**
Disable dq_only flag by default in modelopt onnx quantization

## Testing
Able to build and run model with modelopt onnx Python CLI

- **Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)**
and your commits are signed.
- **Is this change backward compatible?**: No
- dq_only is set to False by default
- **Did you write any new necessary tests?**: No
- **Did you add or update any necessary documentation?**: No
- **Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**:
No


<!-- This is an auto-generated comment: release notes by coderabbit.ai
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## Summary by CodeRabbit

## Release Notes

* **Chores**
* Updated quantization default behavior: Q/DQ (Quantize/Dequantize)
nodes are now added by default instead of only Dequantize nodes.

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settings.</sub>

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Signed-off-by: ajrasane <131806219+ajrasane@users.noreply.github.com>

Author: ajrasane

modelopt/onnx/quantization/quantize.py

@@ -223,7 +223,7 @@ def quantize(
     high_precision_dtype: str = "fp16",
     mha_accumulation_dtype: str = "fp16",
     disable_mha_qdq: bool = False,
-    dq_only: bool = True,
+    dq_only: bool = False,
     block_size: int | None = None,
     use_zero_point: bool = False,
     passes: list[str] = ["concat_elimination"],
@@ -302,7 +302,7 @@ def quantize(
         disable_mha_qdq:
             Don't add Q/DQ layers to MatMuls in MHA pattern.
         dq_only:
-            If True (default), only add DQ nodes to the model. If False, add Q/DQ nodes to the model.
+            If True, only add DQ nodes to the model. If False (default), add Q/DQ nodes to the model.
         block_size:
             Block size parameter for int4 quantization.
         use_zero_point: