Which issue does this PR close?

Closes #.

Rationale for this change

Add native support for Spark's slice(array, start, length) expression so it runs on Comet instead of falling back to Spark.

The datafusion-spark crate already ships a SparkSlice, but it is not Spark-compatible: when a negative start lies before the beginning of the array (e.g. slice([a], -2, 2)), it returns the first element instead of an empty array. We can upstream the fix later; for now this PR ships a Comet-local implementation.

What changes are included in this PR?

• native/spark-expr/src/array_funcs/array_slice.rs: new SparkArraySlice UDF (spark_array_slice) implementing Spark's slice semantics, including 1-based indexing, negative-start-from-end, error on start = 0 or length < 0, and clamping length to the array end. Supports both List and LargeList element storage.
• native/spark-expr/src/comet_scalar_funcs.rs: register the new UDF.
• spark/src/main/scala/org/apache/comet/serde/arrays.scala: CometSlice serde casts the start/length args to Long and serialises a call to spark_array_slice, promising containsNull = true to match DataFusion's list nullability.
• spark/src/main/scala/org/apache/comet/serde/QueryPlanSerde.scala: register Slice in arrayExpressions.

How are these changes tested?

• 12 native unit tests in array_slice.rs covering positive / negative / zero / overflowing start, length 0, length past end, null inputs, empty arrays, and the error cases.
• New SQL test file spark/src/test/resources/sql-tests/expressions/array/slice.sql covering all-literal, column + literal, and column-only argument combinations across boolean, tinyint, smallint, int, bigint, float, double, decimal, date, timestamp, timestamp_ntz, string, and nested array element types, plus the negative-start-overflow case that exposed the upstream bug.