Upgrade the migration guide

Author: MaxGekk

docs/sql-migration-guide-upgrade.md

@@ -43,6 +43,8 @@ displayTitle: Spark SQL Upgrading Guide
 
   - In Spark version 2.4 and earlier, if `org.apache.spark.sql.functions.udf(Any, DataType)` gets a Scala closure with primitive-type argument, the returned UDF will return null if the input values is null. Since Spark 3.0, the UDF will return the default value of the Java type if the input value is null. For example, `val f = udf((x: Int) => x, IntegerType)`, `f($"x")` will return null in Spark 2.4 and earlier if column `x` is null, and return 0 in Spark 3.0. This behavior change is introduced because Spark 3.0 is built with Scala 2.12 by default.
 
+  - Since Spark 3.0, the JDBC options `lowerBound` and `upperBound` are converted to TimestampType/DateType values in the same way as casting strings to TimestampType/DateType values. The conversion is based on Proleptic Gregorian calendar, and time zone defined by the SQL config `spark.sql.session.timeZone`. In Spark version 2.4 and earlier, the conversion is based on the hybrid calendar (Julian + Gregorian) and on default system time zone.
+
 ## Upgrading From Spark SQL 2.3 to 2.4
 
   - In Spark version 2.3 and earlier, the second parameter to array_contains function is implicitly promoted to the element type of first array type parameter. This type promotion can be lossy and may cause `array_contains` function to return wrong result. This problem has been addressed in 2.4 by employing a safer type promotion mechanism. This can cause some change in behavior and are illustrated in the table below.