[SPARK-52680][DOCS][FOLLOW-UP] Update migration guide about Python 3.9 dropped

### What changes were proposed in this pull request?
Update migration guide about Python 3.9 dropped

### Why are the changes needed?
To guide end users

### Does this PR introduce _any_ user-facing change?
yes, doc-only changes

### How was this patch tested?
CI

### Was this patch authored or co-authored using generative AI tooling?
No

Closes #51631 from zhengruifeng/doc_py_39.

Authored-by: Ruifeng Zheng <ruifengz@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>

Author: zhengruifeng

python/docs/source/migration_guide/pyspark_upgrade.rst

@@ -22,12 +22,10 @@ Upgrading PySpark
 Upgrading from PySpark 4.0 to 4.1
 ---------------------------------
 
+* In Spark 4.1, Python 3.9 support was dropped in PySpark.
 * In Spark 4.1, ``DataFrame['name']`` on Spark Connect Python Client no longer eagerly validate the column name. To restore the legacy behavior, set ``PYSPARK_VALIDATE_COLUMN_NAME_LEGACY`` environment variable to ``1``.
-
 * In Spark 4.1, Arrow-optimized Python UDF supports UDT input / output instead of falling back to the regular UDF. To restore the legacy behavior, set ``spark.sql.execution.pythonUDF.arrow.legacy.fallbackOnUDT`` to ``true``.
-
 * In Spark 4.1, unnecessary conversion to pandas instances is removed when ``spark.sql.execution.pythonUDF.arrow.enabled`` is enabled. As a result, the type coercion changes when the produced output has a schema different from the specified schema. To restore the previous behavior, enable ``spark.sql.legacy.execution.pythonUDF.pandas.conversion.enabled``.
-
 * In Spark 4.1, unnecessary conversion to pandas instances is removed when ``spark.sql.execution.pythonUDTF.arrow.enabled`` is enabled. As a result, the type coercion changes when the produced output has a schema different from the specified schema. To restore the previous behavior, enable ``spark.sql.legacy.execution.pythonUDTF.pandas.conversion.enabled``.
 
 Upgrading from PySpark 3.5 to 4.0