[MINOR][DOCS] Add missing backticks in `Upgrading from PySpark 3.5 to…

… 4.0`

nit

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

### Why are the changes needed?
Add missing backticks in `Upgrading from PySpark 3.5 to 4.0`

see https://apache.github.io/spark/api/python/migration_guide/pyspark_upgrade.html

### Does this PR introduce _any_ user-facing change?
to make the doc correctly rendered

### How was this patch tested?
ci

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

Closes #49989 from zhengruifeng/py_fix_doc_upgrade.

Authored-by: Ruifeng Zheng <[email protected]>
Signed-off-by: Hyukjin Kwon <[email protected]>

python/docs/source/migration_guide/pyspark_upgrade.rst

@@ -40,7 +40,7 @@ Upgrading from PySpark 3.5 to 4.0
 * In Spark 4.0, ``include_start`` and ``include_end`` parameters from ``DataFrame.between_time`` have been removed from pandas API on Spark, use ``inclusive`` instead.
 * In Spark 4.0, ``include_start`` and ``include_end`` parameters from ``Series.between_time`` have been removed from pandas API on Spark, use ``inclusive`` instead.
 * In Spark 4.0, the various datetime attributes of ``DatetimeIndex`` (``day``, ``month``, ``year`` etc.) are now ``int32`` instead of ``int64`` from pandas API on Spark.
-* In Spark 4.0, ``sort_columns`` parameter from ``DataFrame.plot`` and `Series.plot`` has been removed from pandas API on Spark.
+* In Spark 4.0, ``sort_columns`` parameter from ``DataFrame.plot`` and ``Series.plot`` has been removed from pandas API on Spark.
 * In Spark 4.0, the default value of ``regex`` parameter for ``Series.str.replace`` has been changed from ``True`` to ``False`` from pandas API on Spark. Additionally, a single character ``pat`` with ``regex=True`` is now treated as a regular expression instead of a string literal.
 * In Spark 4.0, the resulting name from ``value_counts`` for all objects sets to ``'count'`` (or ``'proportion'`` if ``normalize=True`` was passed) from pandas API on Spark, and the index will be named after the original object.
 * In Spark 4.0, ``squeeze`` parameter from ``ps.read_csv`` and ``ps.read_excel`` has been removed from pandas API on Spark.
@@ -72,8 +72,8 @@ Upgrading from PySpark 3.5 to 4.0
 * In Spark 4.0, ``pyspark.testing.assertPandasOnSparkEqual`` has been removed from Pandas API on Spark, use ``pyspark.pandas.testing.assert_frame_equal`` instead.
 * In Spark 4.0, the aliases ``Y``, ``M``, ``H``, ``T``, ``S`` have been deprecated from Pandas API on Spark, use ``YE``, ``ME``, ``h``, ``min``, ``s`` instead respectively.
 * In Spark 4.0, the schema of a map column is inferred by merging the schemas of all pairs in the map. To restore the previous behavior where the schema is only inferred from the first non-null pair, you can set ``spark.sql.pyspark.legacy.inferMapTypeFromFirstPair.enabled`` to ``true``.
-* In Spark 4.0, `compute.ops_on_diff_frames` is on by default. To restore the previous behavior, set `compute.ops_on_diff_frames` to `false`.
-* In Spark 4.0, the data type `YearMonthIntervalType` in ``DataFrame.collect`` no longer returns the underlying integers. To restore the previous behavior, set ``PYSPARK_YM_INTERVAL_LEGACY`` environment variable to ``1``.
+* In Spark 4.0, ``compute.ops_on_diff_frames`` is on by default. To restore the previous behavior, set ``compute.ops_on_diff_frames`` to ``false``.
+* In Spark 4.0, the data type ``YearMonthIntervalType`` in ``DataFrame.collect`` no longer returns the underlying integers. To restore the previous behavior, set ``PYSPARK_YM_INTERVAL_LEGACY`` environment variable to ``1``.
 * In Spark 4.0, items other than functions (e.g. ``DataFrame``, ``Column``, ``StructType``) have been removed from the wildcard import ``from pyspark.sql.functions import *``, you should import these items from proper modules (e.g. ``from pyspark.sql import DataFrame, Column``, ``from pyspark.sql.types import StructType``).