fix: validate map constructor row lengths and null short-circuiting - #5846

Which issue does this PR close?

Follow-up to #5045, addressing additional correctness failures in map_from_arrays.

Rationale for this change

map_from_arrays pairs each row's keys with that same row's values. Spark requires both arrays to have equal lengths. Comet currently checks the combined data for a batch, allowing mismatches in individual rows to cancel out:

Both columns contain four elements in total, so construction succeeds even though neither row is valid. The value 20 crosses into the second row's map. Spark rejects this input with a length-mismatch error; Comet should do the same.

There is also an evaluation-order problem. When the keys array is null, Spark returns a null map immediately. For example, consider a batch containing k = 0 and k = 1:

SELECT map_from_arrays(
 CASE WHEN k = 0 THEN CAST(NULL AS ARRAY<INT>) ELSE array(1) END,
 array(1 / k))
FROM input

With ANSI mode enabled, Spark returns null for k = 0 without evaluating 1 / 0. Comet can evaluate the values expression for that row and fail the entire query, even though no map needs to be constructed.

What changes are included in this PR?

The native path now checks key/value lengths within each non-null row before constructing maps. The same rule applies when one operand is a constant array repeated across the batch, so combining constants and columns cannot hide a mismatch.

Map construction also follows Spark's null-handling order: check the keys first, evaluate the values only for rows with non-null keys, and construct maps only where both arrays exist. The existing constructor handles the resulting valid inputs. This fixes the two examples above without expanding the set of supported map types or casts. Evaluating nondeterministic children exactly once remains a separate issue in #5781.

How are these changes tested?

On the original PR revision, the Linux Rust test job passed, including native coverage for row-length mismatches, constant/column combinations, nulls, empty batches, and sliced inputs. All six new Spark regressions passed in the Spark 4.1 expression job, with JVM codegen dispatch enabled and disabled.

That run's only failed test was a shared decimal codegen coverage assertion, now corrected by upstream #5849 and included in this branch. The exact assertion failed locally before that correction and passed afterward. Full Spark 4.1 reactor compilation, Scalastyle, and Spotless passed on the updated branch. See fresh CI for revision f3ee8d91 for the complete test matrix.