[MLIR] GroupBy JIT cooperative block reductions

Add the pure-MLIR cooperative reduction backend used by the JIT GroupBy.apply
engine, as a standalone building block (no GroupBy.apply wiring yet):

- cooperative_types: CooperativeArrayType {meminfo, data, size, index} + model.
- cooperative_typing: reduction attributes (min/max/sum/mean/var/std/idxmin/
  idxmax/corr), unsupported-dtype columns modelled as Poison, and rejection of
  operators applied directly to a grouped column.
- cooperative_device_funcs: per-dtype MLIR reduction modules (shared-memory tree
  reductions) with NaN skipping (pandas skipna) for min/max/sum, int->int64 sum
  promotion, float32 mean/var/std, ddof=1 var/std, Pearson corr (int-only), and
  (value,index) idxmin/idxmax with smallest-index tie-break.
- cooperative_lowering: thin lowerings that call the outlined device functions,
  plus the linker hook that adds the needed PTX at complete().

Self-contained device tests (test_cooperative_reductions.py) launch one
cooperative block per reduction and verify the scalar result, covering all four
numeric dtypes, NaN skipping, idxmin/idxmax, corr (incl. the float->UDFError
case), and operator rejection. compute-sanitizer memcheck clean.

Author: brandon-b-miller