[spark][flink] Support bucket-level compaction for fixed-bucket tables

[lilei1128]

Purpose

For non-partitioned tables with many buckets, or partitioned tables where each partition
contains many buckets, compacting all buckets in one job may consume excessive planning
and execution resources and eventually cause OOM errors.

This PR introduces bucket-level compaction for fixed-bucket tables in Flink and Spark.
Users can split a large compaction into multiple jobs by selecting individual bucket IDs
or bucket ranges.

For example:

    CALL sys.compact(
      `table` => 'database.large_bucket_table',
      compact_strategy => 'full',
      buckets => '0-999'
  );

A table with many buckets can be compacted in batches such as 0-999, 1000-1999, and 2000-2999.

Fix #9218

Tests

• ParameterUtilsTest#testParseIntegerRanges

• ParameterUtilsTest#testInvalidIntegerRanges

• CompactActionITCase#testCompactSpecifiedBucketRangesFromAction

• CompactProcedureITCase#testCompactSpecifiedBucketRanges

• CompactProcedureTestBase.scala

  • Paimon Procedure: compact specified bucket ranges
  • Paimon Procedure: reject buckets for dynamic bucket table