Yep, I do agree with you that it's not production ready workaround. But I don't think you will be able to find any valuable one either. 

Serverless doesn't have access to add API and does not support setting most Spark properties for notebooks or jobs, as you can read here:

https://docs.databricks.com/aws/en/spark/conf#configure-spark-properties-for-serverless-notebooks-an...

So your options are really limited here. With serverless the assumption is that the optimization part is done for you by Databricks.

But as we can see based on your case, it doesn't always work as expected.

Maybe for that particular job consider using classic compute?