Add a string to a column only when the value matches a condition in pyspark

I got a date column in string format in a PySpark dataframe in below format.

| Date |
| -------- |
| 3/28/2023|

and want my output to be:

| Date |
| -------- |
| 2023-03-28|

I replaced the '/' symbol to '-' and convert the string to date format.

from pyspark.sql import functions as F 
df = df.withColumn("Date", F.regexp_replace('Date', '/', '-'))\
        .withColumn("Date", F.date_format(F.to_date(F.col("Date"),"MM-dd-yyyy"), 'yyyy-MM-dd'))

But I got below error:

SparkUpgradeException: You may get a different result due to the upgrading of Spark 3.0: Fail to parse '3-28-2023' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
Caused by: DateTimeParseException: Text '3-28-2023' could not be parsed at index 0

So to avoid that, I added below line to the code where it adds '0' at the begining of the string and it executed successfully.

df = df.withColumn("Date", F.regexp_replace('Date', '/', '-'))\
       .withColumn('Date', F.concat(F.lit("0"), F.col('Date')))\
       .withColumn("Date", F.date_format(F.to_date(F.col("Date"),"MM-dd-yyyy"), 'yyyy-MM-dd'))

Now, I want to add this zero conditionally such that if only one digit exists before first '-' in the string. I got stuck here because I am not sure if regex expression can aggregate how many values are present before a character. Please help here.