The result is strange when casting `string` to `date` in ORC reading via spark (Schema Evolution) about orc HOT 6 CLOSED

scala> sql("select cast('2022-01-32' as DATE)").show()
+------------------------+
|CAST(2022-01-32 AS DATE)|
+------------------------+
|                    null|
+------------------------+


scala> sql("select cast('9808-02-30' as DATE)").show()
+------------------------+
|CAST(9808-02-30 AS DATE)|
+------------------------+
|                    null|
+------------------------+


scala> sql("select cast('2022-06-31' as DATE)").show()
+------------------------+
|CAST(2022-06-31 AS DATE)|
+------------------------+
|                    null|
+------------------------+

 * The reader schema is said to be evolved (or projected) when it changed after the data is
 * written by writers. The followings are supported in file-based data sources.
 * Note that partition columns are not maintained in files. Here, `column` means non-partition
 * column.
 *
 *   1. Add a column
 *   2. Hide a column
 *   3. Change a column position
 *   4. Change a column type (Upcast)
 *
 * Here, we consider safe changes without data loss. For example, data type changes should be
 * from small types to larger types like `int`-to-`long`, not vice versa.
 *
 * So far, file-based data sources have the following coverages.
 *
 *   | File Format  | Coverage     | Note                                                   |
 *   | ------------ | ------------ | ------------------------------------------------------ |
 *   | TEXT         | N/A          | Schema consists of a single string column.             |
 *   | CSV          | 1, 2, 4      |                                                        |
 *   | JSON         | 1, 2, 3, 4   |                                                        |
 *   | ORC          | 1, 2, 3, 4   | Native vectorized ORC reader has the widest coverage.  |
 *   | PARQUET      | 1, 2, 3      |                                                        |
 *   | AVRO         | 1, 2, 3      |                                                        |

scala> sql("set spark.sql.orc.impl=hive")

scala> :paste
// Entering paste mode (ctrl-D to finish)

val data = Seq(
    ("", "2022-01-32"),  // pay attention to this, null
    ("", "9808-02-30"),  // pay attention to this, 9808-02-29
    ("", "2022-06-31"),  // pay attention to this, 2022-06-30
)
val cols = Seq("str", "date_str")
val df = spark.createDataFrame(data).toDF(cols:_*).repartition(1)
df.write.format("orc").mode("overwrite").save("/tmp/df")
spark.read.format("orc").schema("date_str date").load("/tmp/df").show(false)

// Exiting paste mode, now interpreting.

+--------+
|date_str|
+--------+
|null    |
|null    |
|null    |
+--------+