[SPARK-58646][PS][FOLLOWUP] Fix NumPy reciprocal parity for decimal, boolean, and narrower integer columns

[Yicong-Huang]

What changes were proposed in this pull request?

This is a follow-up of #57856 (SPARK-58646), which replaced the scalar pandas UDF fallback of np.reciprocal with native Spark expressions for non-floating-point inputs.

That change routed every non-float/double dtype through a single integer branch that hard-codes the int64 minimum as the divide-by-zero sentinel and casts the quotient through long. This does not match the previous pandas UDF (np.reciprocal applied to the pandas Series) for decimals, booleans, and narrower integers:

dtype	input	previous UDF (np.reciprocal -> Double)	merged native (#57856)
int64 (bigint)	0	-9.2e18 (int64 min)	-9.2e18 (unchanged)
int32 (int)	0	-2147483648 (int32 min)	-9.2e18
int8/int16 (tinyint/smallint)	0	0 (numpy 1 // 0 does not overflow on narrow widths)	-9.2e18
boolean	False	0.0 (numpy promotes bool to int8: True -> 1, False -> 0)	-9.2e18
decimal	2.5	0.4 (numpy takes a true floating reciprocal)	0.0 (truncated to long)

This PR restores parity:

• Decimal inputs now flow through the floating-point reciprocal branch (typeof starts with decimal), since numpy computes a true reciprocal for them. A decimal 0 (which the old UDF could not handle -- np.reciprocal(Decimal('0')) raises DivisionByZero) now maps to inf, consistent with the floating-point branch.
• The integer/boolean branch now picks the divide-by-zero sentinel by column width -- int32 minimum for int, int64 minimum for bigint, and 0 for the narrower widths (tinyint, smallint, and boolean promoted to int8) -- and casts through long so boolean and narrower integers can take part in the division.

int64 columns, the only case exercised by the original PR, are unchanged.

Why are the changes needed?

The merged native expression regressed the observable pandas-on-Spark behavior for decimal, boolean, and narrower-integer columns relative to the pandas UDF it replaced. This restores parity so that np.reciprocal produces the same results as before across all supported dtypes.

Does this PR introduce any user-facing change?

No. #57856 is unreleased (master only), so this only fixes an unreleased regression before it ships; there is no change relative to any released Spark version.

How was this patch tested?

Added test_np_reciprocal_non_default_dtypes in python/pyspark/pandas/tests/test_numpy_compat.py, inherited by the Spark Connect parity suite, asserting np.reciprocal(psser) equals np.reciprocal(pdf) for int8/int16/int32, boolean, and decimal columns (covering positive, negative, and the per-width zero-overflow sentinel).

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

No.