[SQL] Row-based Parquet reader silently accepts incompatible primitive type conversions

[Jiayi-Wang-db]

Problem

Spark's row-based Parquet reader and vectorized Parquet reader handle some incompatible requested
schemas differently. The vectorized reader raises SchemaColumnConvertNotSupportedException, while
the row-based reader silently interprets the physical value as the requested Catalyst type.

Two examples are:

Parquet file type	Requested Spark type	Row-based reader	Vectorized reader
FIXED_LEN_BYTE_ARRAY(4)	STRING	Returns the raw bytes as UTF-8	Rejects the conversion
INT32 (DATE)	DECIMAL(10, 0)	Returns the day count as a decimal	Rejects the conversion

The row reader's ParquetRowConverter currently accepts every binary-like primitive as a string
and treats any INT32 or INT64 without decimal metadata as an unannotated integer-backed
decimal. The latter ignores semantic logical annotations such as DATE.

This is a correctness problem because changing spark.sql.parquet.enableVectorizedReader can
change a query from failing cleanly to returning incorrectly interpreted data.

Expected behavior

Both readers should reject these unsupported conversions with
FAILED_READ_FILE.PARQUET_COLUMN_DATA_TYPE_MISMATCH.

Supported conversions should remain unchanged, including Parquet BINARY to Spark STRING and
unannotated or signed-integer INT32/INT64 to a sufficiently compatible Spark decimal type.

Related discussions

• [SPARK-34212][SQL] Fix incorrect decimal reading from Parquet files #31319 / SPARK-34212 established the decimal conversion rules and discussed consistency between
  the row-based and vectorized readers.
• [SPARK-40876][SQL] Widening type promotions in Parquet readers #44368 / SPARK-40876 added lossless widening conversions to both readers and required logical
  annotation checks for semantic types such as DATE.
• [WIP][SPARK-32317][SQL]Conversion from parquet original data type to spark data type #29600 / SPARK-32317 proposed a broader configurable conversion matrix but was not merged.