Change value in nested struct, array, struct in a spark dataframe using pyspark

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spark

So I have this spark dataframe with following schema:

```

root
 |-- id: string (nullable = true)
 |-- elements: struct (nullable = true)
 |    |-- created: string (nullable = true)
 |    |-- id: string (nullable = true)
 |    |-- items: array (nullable = true)
 |    |    |-- element: struct (containsNull = true)
 |    |    |    |-- field: string (nullable = true)
 |    |    |    |-- fieldId: string (nullable = true)
 |    |    |    |-- fieldtype: string (nullable = true)
 |    |    |    |-- from: string (nullable = true)
 |    |    |    |-- fromString: string (nullable = true)
 |    |    |    |-- tmpFromAccountId: string (nullable = true)
 |    |    |    |-- tmpToAccountId: string (nullable = true)
 |    |    |    |-- to: string (nullable = true)
 |    |    |    |-- toString: string (nullable = true)

For this case, I want to change value inside "items" elements (field, fieldId, etc.) using defined value ("Issue") - without caring if it is empty or already filled. So it should be from:

+--------+--------------------------------------------------------------------------------+
| id     | elements                                                                       |
+--------+--------------------------------------------------------------------------------+
|ABCD-123|[2023-01-16T20:25:30.875+0700, 5388402, [[field, , status,,,,, 23456, Yes]]]    |
+--------+--------------------------------------------------------------------------------+

To:

+--------+----------------------------------------------------------------------------------------------------------+
| id     | elements                                                                                                 |
+--------+----------------------------------------------------------------------------------------------------------+
|ABCD-123|[2023-01-16T20:25:30.875+0700, 5388402, [[Issue, Issue, Issue, Issue, Issue, Issue, Issue, Issue, Issue]]]|
+-------------------------------------------------------------------------------------------------------------------+

What I have tried:

I already try using this script in python file, but it didn't work:

replace_list = ['field', 'fieldtype', 'fieldId', 'from', 'fromString', 'to', 'toString', 'tmpFromAccountId', 'tmpToAccountId']

# Didn't work 1
for col_name in replace_list: df = df.withColumn(f"items.element.{col_name}", lit("Issue"))

# Didn't work 2
for col_name in replace_list: df = df.withColumn("elements.items.element", struct(col(f"elements.items.element.*"), lit("Issue").alias(f"{col_name}")))

In this case, I'm using Spark version 2.4.8. I don't want to use explode method since I want to avoid join dataframes. Is it possible to perform this kind of operation directly in spark? Thank you.