Unable to read a table with invalid dates using SQL endpoint

Hi There,
We have some source system which treat NULL values as '0001-01-01' and some dates are before '1900-01-01'. When we try to read such data written using lakehouse notebook via SQL endpoint, we are getting unable to run query error. Has anyone faced this issue and overcome it.

I have attached a sample notebook which reproduces the issue.

from pyspark.sql.functions import *
from pyspark.sql.types import DateType

spark.conf.set("spark.sql.parquet.int96RebaseModeInRead", "LEGACY")
spark.conf.set("spark.sql.parquet.int96RebaseModeInWrite", "LEGACY")
spark.conf.set("spark.sql.parquet.datetimeRebaseModeInRead", "LEGACY")
spark.conf.set("spark.sql.parquet.datetimeRebaseModeInWrite", "LEGACY")

new_df = spark.createDataFrame([('0065-04-01',),('0001-01-01',),('1993-03-11',)], ['COVER_END_DATE'])
new_df.select(col('COVER_END_DATE')).show()

spark.sql(f"CREATE TABLE IF NOT EXISTS test_date_issue2(COVER_END_DATE DATE) using DELTA LOCATION 'Tables/test_date_issue2'")

new_df.createOrReplaceTempView("TARGET_VIEW")

spark.sql(f"INSERT INTO test_date_issue2 SELECT * FROM TARGET_VIEW")

Thanks,

Chetna