[SPARK-57645][PYTHON][TESTS] Add ASV microbenchmark for SQL_GROUPED_AGG_PANDAS_ITER_UDF

[Yicong-Huang]

What changes were proposed in this pull request?

Add an ASV microbenchmark for SQL_GROUPED_AGG_PANDAS_ITER_UDF to python/benchmarks/bench_eval_type.py, parallel to the existing GroupedAggArrowIterUDFTimeBench. New classes: _GroupedAggPandasIterBenchMixin, GroupedAggPandasIterUDFTimeBench, and GroupedAggPandasIterUDFPeakmemBench. The mixin reuses _write_scenario/_build_scenario/_scenario_configs from the non-iterator Pandas sibling and only overrides the eval type and the iterator-style UDFs (sum_udf, mean_multi_udf) that consume an Iterator[pd.Series].

Why are the changes needed?

SQL_GROUPED_AGG_PANDAS_ITER_UDF had no worker-level microbenchmark. This fills the coverage gap and provides a before/after baseline for an upcoming serializer refactor of this eval type.

Does this PR introduce any user-facing change?

No.

How was this patch tested?

Existing tests. Benchmark-only addition. The worker output of the new iterator bench was verified to be byte-identical to the non-iterator Pandas grouped-agg bench across all scenario/UDF combinations (only the trailing timing telemetry differs).

ASV results (COLUMNS=120 asv run --bench GroupedAggPandasIterUDFTimeBench -a repeat=3 --python=same):

bench_eval_type.GroupedAggPandasIterUDFTimeBench.time_worker
================ ============ ================
--                            udf
---------------- -----------------------------
    scenario       sum_udf     mean_multi_udf
================ ============ ================
 few_groups_sm    40.6+-0.3ms    46.9+-0.4ms
 few_groups_lg    66.5+-0.4ms    74.7+-0.4ms
 many_groups_sm   1.53+-0.01s    1.77+-0.01s
 many_groups_lg    428+-3ms       487+-1ms
   wide_cols      397+-0.9ms      420+-2ms
================ ============ ================

Numbers are stable across two local runs (deltas < 3%).

Was this patch authored or co-authored using generative AI tooling?

No.

[uros-b]

The worker output of the new iterator bench was verified to be byte-identical to the non-iterator Pandas grouped-agg bench

Minor note regarding the PR description, please confirm - in worker.py: the non-iterator SQL_GROUPED_AGG_PANDAS_UDF writes via ArrowStreamGroupSerializer(write_start_stream=True) while the ITER variant uses ArrowStreamAggPandasUDFSerializer; genuinely different output serializers/markers, so the byte streams are not identical. Please update in order to avoid misleading a future reader.

[uros-b]

Thank you @Yicong-Huang!