[SPARK-33641][SQL][DOC][FOLLOW-UP] Add migration guide for CHAR VARCH…

…AR types

### What changes were proposed in this pull request?

Add migration guide for CHAR VARCHAR types

### Why are the changes needed?

for migration

### Does this PR introduce _any_ user-facing change?

doc change

### How was this patch tested?

passing ci

Closes #30654 from yaooqinn/SPARK-33641-F.

Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit c88edda)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>

docs/sql-migration-guide.md

@@ -54,6 +54,8 @@ license: |
 
   - In Spark 3.1, creating or altering a view will capture runtime SQL configs and store them as view properties. These configs will be applied during the parsing and analysis phases of the view resolution. To restore the behavior before Spark 3.1, you can set `spark.sql.legacy.useCurrentConfigsForView` to `true`.
 
+  - Since Spark 3.1, CHAR/CHARACTER and VARCHAR types are supported in the table schema. Table scan/insertion will respect the char/varchar semantic. If char/varchar is used in places other than table schema, an exception will be thrown (CAST is an exception that simply treats char/varchar as string like before). To restore the behavior before Spark 3.1, which treats them as STRING types and ignores a length parameter, e.g. `CHAR(4)`, you can set `spark.sql.legacy.charVarcharAsString` to `true`.
+
 ## Upgrading from Spark SQL 3.0 to 3.0.1
 
 - In Spark 3.0, JSON datasource and JSON function `schema_of_json` infer TimestampType from string values if they match to the pattern defined by the JSON option `timestampFormat`. Since version 3.0.1, the timestamp type inference is disabled by default. Set the JSON option `inferTimestamp` to `true` to enable such type inference.