spark timestamp timezone in JDBC read/write

I am creating a parquet file from reading data from oracle.

Oracle is running in UTC. I confirmed using,

SELECT DBTIMEZONE FROM DUAL;

Output:

DBTIMEZONE|
----------|
+00:00    |

Reading from JDBC and writing to S3 as parquet:

df = spark.read.format('jdbc').options(url=url,
                                           dbtable=query,
                                           user=user,
                                           password=password,
                                           fetchsize=2000).load()

df.write.parquet(s3_loc, mode="overwrite")

Now, I checked value of spark.sql.session.timeZone

print(spark.conf.get("spark.sql.session.timeZone"))

Output:

UTC

Now, I am reading data from S3 location:

df1 = spark.read.parquet(s3_loc)
df1.show()

Output:

+-------------------+
|             col1  |
+-------------------+
|2012-11-11 05:00:00|
|2013-11-25 05:00:00|
|2013-11-11 05:00:00|
|2014-12-25 05:00:00|
+-------------------+

col1 is date in oracle and converted to timestamp in spark df.

Why 5 hours are added in the output? Database is running in UTC and spark.sql.session.timeZone is UTC.

Note:

1. Both RDS and EMR are running in AWS US-EAST-1
2. On all the spark nodes, I ran TZ=UTC