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[SPARK-29774][SQL][FOLLOWUP] Add a migration guide for date_add and date_sub #26932

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[SPARK-29774][SQL][FOLLOWUP] Add a migration guide for date_add and date_sub #26932

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2 changes: 2 additions & 0 deletions docs/sql-migration-guide.md
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- Since Spark 3.0, day-time interval strings are converted to intervals with respect to the `from` and `to` bounds. If an input string does not match to the pattern defined by specified bounds, the `ParseException` exception is thrown. For example, `interval '2 10:20' hour to minute` raises the exception because the expected format is `[+|-]h[h]:[m]m`. In Spark version 2.4, the `from` bound was not taken into account, and the `to` bound was used to truncate the resulted interval. For instance, the day-time interval string from the showed example is converted to `interval 10 hours 20 minutes`. To restore the behavior before Spark 3.0, you can set `spark.sql.legacy.fromDayTimeString.enabled` to `true`.

- Since Spark 3.0, the `date_add` and `date_sub` functions only accepts int, smallint, tinyint as the 2nd argument, fractional and string types are not valid anymore, e.g. `date_add(cast('1964-05-23' as date), '12.34')` will cause `AnalysisException`. In Spark version 2.4 and earlier, if the 2nd argument is fractional or string value, it will be coerced to int value, and the result will be a date value of `1964-06-04`.

## Upgrading from Spark SQL 2.4 to 2.4.1

- The value of `spark.executor.heartbeatInterval`, when specified without units like "30" rather than "30s", was
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