[SPARK-25797][SQL][DOCS][BACKPORT-2.3] Add migration doc for solving issues caused by view canonicalization approach change

## What changes were proposed in this pull request?
Since Spark 2.2, view definitions are stored in a different way from prior versions. This may cause Spark unable to read views created by prior versions. See [SPARK-25797](https://issues.apache.org/jira/browse/SPARK-25797) for more details.

Basically, we have 2 options.
1) Make Spark 2.2+ able to get older view definitions back. Since the expanded text is buggy and unusable, we have to use original text (this is possible with [SPARK-25459](https://issues.apache.org/jira/browse/SPARK-25459)). However, because older Spark versions don't save the context for the database, we cannot always get correct view definitions without view default database.
2) Recreate the views by `ALTER VIEW AS` or `CREATE OR REPLACE VIEW AS`.

This PR aims to add migration doc to help users troubleshoot this issue by above option 2.

## How was this patch tested?
N/A.

Docs are generated and checked locally

```
cd docs
SKIP_API=1 jekyll serve --watch
```

Closes #22851 from seancxmao/SPARK-25797-2.3.

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

Author: seancxmao

docs/sql-programming-guide.md

@@ -1973,6 +1973,8 @@ working with timestamps in `pandas_udf`s to get the best performance, see
 
   - Since Spark 2.2.1 and 2.3.0, the schema is always inferred at runtime when the data source tables have the columns that exist in both partition schema and data schema. The inferred schema does not have the partitioned columns. When reading the table, Spark respects the partition values of these overlapping columns instead of the values stored in the data source files. In 2.2.0 and 2.1.x release, the inferred schema is partitioned but the data of the table is invisible to users (i.e., the result set is empty).
 
+  - Since Spark 2.2, view definitions are stored in a different way from prior versions. This may cause Spark unable to read views created by prior versions. In such cases, you need to recreate the views using `ALTER VIEW AS` or `CREATE OR REPLACE VIEW AS` with newer Spark versions.
+
 ## Upgrading From Spark SQL 2.0 to 2.1
 
  - Datasource tables now store partition metadata in the Hive metastore. This means that Hive DDLs such as `ALTER TABLE PARTITION ... SET LOCATION` are now available for tables created with the Datasource API.