perf: vectorize spark_unscaled_value (9x faster)

[u70b3]

Which issue does this PR close?

Part of #4942.

Rationale for this change

spark_unscaled_value is injected by Spark's DecimalAggregates rule for every sum/avg over narrow decimals, so it sits on the TPC-DS decimal aggregation hot path. The array branch used a per-element Int64Builder loop.

What changes are included in this PR?

• Replace the per-element builder loop with the vectorized Arrow unary kernel: the Decimal128 values buffer is mapped to Int64 in a single pass and the null buffer is carried over untouched (zero-copy). The scalar branch is unchanged.
• Add a criterion benchmark (benches/unscaled_value.rs) covering no-null / sparse-null (1 in 10) / dense-null (1 in 2) shapes at 8192 rows, following the scalar expression optimization guide.

Benchmark results (8192 rows, Decimal128(20,2), median):

shape	before	after	speedup
no nulls	29.5 µs	3.2 µs	~9.3x
sparse nulls (1/10)	35.5 µs	3.0 µs	~11.7x
dense nulls (1/2)	30.5 µs	2.9 µs	~10.4x

How are these changes tested?

• Added unit tests for the array path (values and null distribution are bit-exact, including nulls) and the scalar path; the function previously had no direct tests.
• Full datafusion-comet-spark-expr suite passes (497 tests), plus clippy --all-targets -D warnings and fmt.
• Benchmark shows a strict improvement on all three shapes with no regression (criterion change −89% to −92%, p=0.00).

[andygrove]

LGTM. Thanks @u70b3