[spark] Support configurable dynamic partition column order

[sablejade]

Purpose

This PR fixes a Spark dynamic partition write issue where positional writes may be incorrectly interpreted as Hive-style dynamic partition writes.

When writing to a partitioned Paimon table with Spark SQL dynamic partition syntax, the query output may already follow the target table schema order. However, the current logic may still treat it as Hive-style order and move dynamic partition columns from the tail. This can misalign columns, especially for UNION queries whose output names are inherited from the first branch.

For example, a UNION query may output columns in the correct table schema order, but one expression name, such as detail_ratio, does not match the target column name value. The write is positional, so the value should still be written to the value column. Relying only on output names can misclassify the query order and cause silent column misalignment when column types are compatible.

This PR adds a configurable dynamic partition column order mode:

• AUTO: preserve compatible behavior and automatically detect table-order or Hive-style-order writes.
• TABLE: always interpret positional dynamic partition writes using the target table schema order.
• HIVE: interpret dynamic partition writes using Hive-style order when applicable.

Changes

• Add spark.paimon.sql.dynamic-partition-column-order.
• Support AUTO, TABLE, and HIVE modes.
• Avoid unnecessary Hive-style reordering when the query already follows table schema order.
• Make dynamic overwrite without explicit PARTITION (...) respect the configured column order when Hive-style output is applicable.
• Add Spark SQL tests for:
  • UNION output whose expression names differ from target column names.
  • AUTO table-order detection.
  • AUTO/HIVE Hive-style detection.
  • Explicit TABLE mode.
  • Explicit HIVE mode.
  • INSERT BY NAME behavior.
  • Invalid config handling.

Tests

• PaimonDynamicPartitionColumnOrderTest

Closes #9156