How to correctly provide log4j properties file for pyspark glue jobs?

Following this post: https://repost.aws/knowledge-center/glue-reduce-cloudwatch-logs I have created the following glue job:

from awsglue.context import GlueContext
from pyspark.context import SparkContext
import logging
from pyspark.sql.functions import rand, randn

DEFAULT_LOG_LEVEL = logging.DEBUG
DEFAULT_LOG_FORMAT = "%(asctime)s - %(levelname)-8s: %(message)s"
class SparkLogger(logging.Handler):
    def __init__(self, j_logger) -> None:
        self.j_logger = j_logger
        logging.Handler.__init__(self=self)

    def emit(self, record: logging.LogRecord) -> None:
        print(record)
        self.j_logger.log(None, None, record.levelno, logging.Handler.format(self, record), None, None)

def get_logger(
    name: str, level: int = DEFAULT_LOG_LEVEL, format: str = DEFAULT_LOG_FORMAT
) -> logging.Logger:
    logger = logging.getLogger(name)
    logger.setLevel(level)
    glueContext = GlueContext(SparkContext.getOrCreate())
    handler = SparkLogger(glueContext.get_logger().log())
    handler.setLevel(level)
    handler.setFormatter(logging.Formatter(fmt=format, datefmt="%Y-%m-%d %H:%M:%S"))
    logger.addHandler(handler)
    return logger
    
def main():
    sc = SparkContext.getOrCreate()
    glueContext = GlueContext(sc)
    logger = get_logger("MEEEJN")
    logger.info("info message")
    logger.warn("warn message")
    logger.error("error message")
    # Create a DataFrame with random data
# Here we are creating a DataFrame with two columns: 'id' and 'value'
# 'id' column will have random integers and 'value' column will have random floats
    spark = glueContext.spark_session
    df = spark.range(0, 100).withColumn('id', (rand(seed=1234) * 100).cast('int')).withColumn('value', randn(seed=42))

# Perform a transformation: Let's filter rows where 'value' > 0 and then add a new column 'value_squared' which is the square of 'value'
    transformed_df = df.filter(df['value'] > 0).withColumn('value_squared', df['value'] ** 2)

# Collect and print the top 6 rows of the transformed DataFrame
    top_rows = transformed_df.take(6)

    print("Top 6 Rows:")
    for row in top_rows:
        print(row)

if __name__ == "__main__":
    main()

Then, at the job details, libraries section in the Referenced files path input, i put the s3 uri of a log4j.properties file that contains the following:

# Root Logger
log4j.rootLogger=DEBUG, stdout
log4j.logger.org.apache.spark.api.python.PythonGatewayServer=DEBUG

# Direct log messages to stdout
log4j.appender.stdout=org.apache.log4j.ConsoleAppender
log4j.appender.stdout.Target=System.out
log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
log4j.appender.stdout.layout.ConversionPattern=%m%n

This, in my understanding should make my continous logs contain only the log message, no date and other information provided. Yet my logs in cloudwatch still look like this:

What am I doing wrong?