[SPARK-25132][SQL][FOLLOWUP][DOC] Add migration doc for case-insensitive field resolution when reading from Parquet

## What changes were proposed in this pull request?
apache#22148 introduces a behavior change. According to discussion at apache#22184, this PR updates migration guide when upgrade from Spark 2.3 to 2.4.

## How was this patch tested?
N/A

Closes apache#23238 from seancxmao/SPARK-25132-doc-2.4.

Authored-by: seancxmao <seancxmao@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>

Author: seancxmao

docs/sql-migration-guide-upgrade.md

@@ -145,6 +145,8 @@ displayTitle: Spark SQL Upgrading Guide
 
   - In Spark version 2.3 and earlier, HAVING without GROUP BY is treated as WHERE. This means, `SELECT 1 FROM range(10) HAVING true` is executed as `SELECT 1 FROM range(10) WHERE true`  and returns 10 rows. This violates SQL standard, and has been fixed in Spark 2.4. Since Spark 2.4, HAVING without GROUP BY is treated as a global aggregate, which means `SELECT 1 FROM range(10) HAVING true` will return only one row. To restore the previous behavior, set `spark.sql.legacy.parser.havingWithoutGroupByAsWhere` to `true`.
 
+  - In version 2.3 and earlier, when reading from a Parquet data source table, Spark always returns null for any column whose column names in Hive metastore schema and Parquet schema are in different letter cases, no matter whether `spark.sql.caseSensitive` is set to `true` or `false`. Since 2.4, when `spark.sql.caseSensitive` is set to `false`, Spark does case insensitive column name resolution between Hive metastore schema and Parquet schema, so even column names are in different letter cases, Spark returns corresponding column values. An exception is thrown if there is ambiguity, i.e. more than one Parquet column is matched. This change also applies to Parquet Hive tables when `spark.sql.hive.convertMetastoreParquet` is set to `true`.
+
 ## Upgrading From Spark SQL 2.3.0 to 2.3.1 and above
 
   - As of version 2.3.1 Arrow functionality, including `pandas_udf` and `toPandas()`/`createDataFrame()` with `spark.sql.execution.arrow.enabled` set to `True`, has been marked as experimental. These are still evolving and not currently recommended for use in production.