Azure Synapse provides purpose-built engines for specific use cases. Apache Spark for Synapse is designed as a job service and not a cluster model.
There are two scenarios where people ask for a multi-user cluster model.
Scenario #1: Many users accessing a cluster for serving data for BI purposes.
The easiest way of accomplishing this task is to cook the data with Spark and then take advantage of the serving capabilities of Synapse SQL to that they can connect Power BI to those datasets.
Scenario #2: Having multiple developers on a single cluster to save money.
To satisfy this scenario, you should give each developer a serverless Spark pool that is set to use a small number of Spark resources. Since serverless Spark pools don't cost anything, until they are actively used minimizes the cost when there are multiple developers. The pools share metadata (Spark tables) so they can easily work with each other.