[DsThakurRawat]

Which issue does this PR close?

• Closes coerce_int96 does not coerce INT96 inside legacy Parquet list layouts (bag/array, two-level lists) #26393.

Rationale for this change

In datafusion/datasource-parquet/src/schema_coercion.rs, Int96Coercer matches leaf column paths against int96_fields by traversing the schema with a DFS stack. When encountering a list field (DataType::List), the previous implementation hard-coded:

child_path.push(".list.");
child_path.push(unprocessed_child.name());

However, Parquet files written by Spark with legacy formatting (spark.sql.parquet.writeLegacyFormat=true), Hive, or older Parquet writers produce alternative list structures:

• Spark legacy 3-level list: repeated group named bag, child element named array (e.g. at.bag.array).
• Hive legacy 3-level list: repeated group named bag, child element named array_element (e.g. at.bag.array_element).
• 2-level list: repeated primitive or group (e.g. at.array).
• Repeated primitive at root or struct level without a synthetic list group.

Because child_path assumed .list.<child_name>, these legacy schemas resulted in non-matching paths such as at.list.array vs at.bag.array, leaving INT96 timestamps as Timestamp(Nanosecond, None) instead of coercing them to the target resolution (e.g., Timestamp(Microsecond, None)).

In addition, container types like DataType::LargeList and DataType::FixedSizeList were previously ignored by Int96Coercer.

What changes are included in this PR?

• Pass Option<&'a ParquetType> in the StackContext tuple during schema DFS traversal to inspect the matching Parquet schema group/primitive types.
• Introduce get_parquet_list_element_info:
  • For primitive repeated fields (2-level lists of primitives or repeated root fields), uses the actual field name without superfluous .list. segments.
  • For 3-level lists, dynamically extracts the repeated group name (list, bag, etc.) and element field name (element, array, array_element, etc.).
  • For 2-level lists of structs with multiple fields, resolves the repeated group directly as the struct element.
  • Gracefully falls back to standard conventions if Parquet type info is missing or unannotated.
• Expand list pattern matching to support DataType::LargeList and DataType::FixedSizeList.
• Check both int96_fields.contains(path) and current_parquet_type.is_some_and(|pt| pt.is_primitive() && pt.get_physical_type() == Type::INT96).

What is the testing strategy for this PR?

• Added coerce_int96_to_resolution_with_legacy_list_layouts unit test covering:
  • standard 3-level (list/element)
  • spark_legacy 3-level (bag/array)
  • hive 3-level (bag/array_element)
  • two_level (array)
  • two_level_struct (array with multiple struct fields)
  • repeated_primitive_root (repeated int96 at)
  • nested_lists_legacy (multi-dimensional list of lists with legacy layouts)
• Added coerce_int96_to_resolution_with_large_and_fixed_lists unit test testing LargeList and FixedSizeList containers.
• Verified all 295 unit tests in datafusion-datasource-parquet pass without regressions.
• Ran cargo fmt --check and cargo clippy -p datafusion-datasource-parquet --all-targets -- -D warnings.

Are there any user-facing changes?

No breaking changes. Queries reading Parquet files with legacy list structures containing INT96 timestamps will now correctly coerce to the configured timestamp resolution instead of retaining nanosecond precision.