Add randomised fuzz harness for pyarrow UDF vector-copy path

## What is the problem the feature request solves?

PR #4234 introduces `CometColumnarPythonInput.copyVector`, which walks Comet's `FieldVector` tree in lockstep with a destination IPC root and `setBytes`-copies each Arrow buffer, then sets value counts bottom-up so offset bytes aren't rewritten. This is exactly the kind of recursive bytewise walk where hand-written cases catch the obvious bugs and miss the long tail.

`CometCodegenFuzzSuite` (#4267) already does the equivalent job for the codegen path: random schema, random data, run an identity UDF over every column, assert row-equivalence. The same pattern translates directly to the pyarrow UDF path.

## Describe the potential solution

A pytest module under `spark/src/test/resources/pyspark/` (alongside `test_pyarrow_udf.py`) that:

1. **Generates random schemas.** A type generator that draws from primitives (Long, Int, Short, Byte, Boolean, Float, Double, Date, Timestamp, TimestampNTZ, String, Binary, Decimal with varied precision/scale) and recurses into ArrayType / StructType / MapType with bounded depth. Seeded for reproducibility.
2. **Generates random data.** Per column, draw a null fraction from `{0, 0.01, 0.5, 0.99, 1.0}` and fill with random values matching the type. Length distribution for variable-width and list types should cover the small + large extremes.
3. **Writes parquet, reads it back through `mapInArrow(passthrough)`**, asserts row-equivalence between accelerated and fallback modes.
4. **Forces multiple Arrow IPC batches per partition** by setting `spark.sql.execution.arrow.maxRecordsPerBatch` low, so the persistent destination IPC root is exercised across batches in every run.

This closes three of the test gaps from @mbutrovich's review on #4234 (items 1, 3, 4 from the test section) in one move: the harness exercises recursive vector-tree walks, validity-bit handling across null densities, and multi-batch per partition without needing per-shape hand-written cases.

The targeted hand-written cases already added in PR #4234 (decimal precision sweep, null density sweep, multi-batch, wide schema, mid-stream empty batch, transforming array UDF) stay — they're cheap to keep and they pin the boundaries that fuzz won't find reliably (the precision sweep, for example, deterministically hits the 18/19 boundary that fuzz might happen to skip).

## Additional context

- Reference test fixture: `spark/src/test/resources/pyspark/test_pyarrow_udf.py` (PR #4234) — accelerated/fallback parametrisation pattern, jar resolution, session setup all reusable.
- Related: PR #4234 (introduces the operator), #4383 (drops the per-batch buffer copy on the same code path — fuzz harness gains value once that's landed, since the new direct-from-Comet path has its own walk to validate), `CometCodegenFuzzSuite` (#4267, harness shape to borrow from).
- Originally surfaced as the first item in @mbutrovich's "Tests" section on #4234.