我正在编写一个spark代码，从一个dataframe插入到一个mysql表中，我得到了一个错误

df_to_write.write.format("jdbc").option("url", jdbc_url).option("driver", "com.mysql.jdbc.Driver")\
          .option("dbtable", tbl_name).option("user", DBUser).option("password", DBPassword)\
          .option("numPartitions",32).mode('append').save()

org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in stage 18.0 failed 4 times, most recent failure: Lost task 1.3 in stage 18.0 (TID 170, 10.151.244.77, executor 0): java.sql.BatchUpdateException: Data truncation: Incorrect datetime value: '1970-01-01 00:00:00' for column

我发现这个是因为列的值是1970-01-01 00:00:00
但是，如果我在mysql workbench中运行insert语句，它将发出警告，并且值将作为0000插入
示例如下：

create table test_time (processing_ts timestamp NULL DEFAULT NULL);

insert into test_time values ('1990-01-01T00:00:00.000+0000'),('1990-01-01T00:00:00.000+0000'),('1970-01-01T00:00:00.000+0000');

表的输出值如下所示

1990-01-01 00:00:00
1990-01-01 00:00:00
0000-00-00 00:00:00     --> no error only warning and changed the value to 0000:00:00 00:00:00

想知道我可以对spark应用什么设置来获得相同的行为，即应该没有错误，并且值应该设置为0000-00-00:00:00。写入表时，是否仍要在spark中应用insert ignore选项。
根据注解，粘贴模拟代码
sql语句

mysql> create table test (processing_ts timestamp null);
Query OK, 0 rows affected (0.05 sec)

mysql> select * from test;
+---------------------+
| processing_ts       |
+---------------------+
| 1997-02-28 10:30:00 |
+---------------------+
1 row in set (0.00 sec)

df = spark.createDataFrame([('1970-01-01 00:00:00',)], ['processing_ts'])
df2 =  df.select(f.to_timestamp(df.processing_ts, 'yyyy-MM-dd HH:mm:ss').alias('processing_ts'))

db_host = '127.0.0.1'
DBName = 'test'
jdbc_url = "jdbc:mysql://{}/{}".format(db_host ,DBName)
DBUser = 'XXXXXX'
DBPassword = 'XXXXXXX123'
tbl_name = 'test'

df2.write.format("jdbc").option("url", jdbc_url).option("driver", "com.mysql.jdbc.Driver")\
          .option("dbtable", tbl_name).option("user", DBUser).option("password", DBPassword)\
          .option("numPartitions",32).mode('append').save()