Integrate Spark operator with Jupyterhub

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#### Please describe your question here
How to integrate Jupyterhub with Spark Operator? Tried to integrate it by installing Jupyterhub and Spark-operator but it's not working

Jupyterhub and Spark operator are running in the same namespace. Attached spark-operator service account to jupyterhub so that it can communicate with Kubernetes APIs. Creating a SparkSession through the following code,

```
import os
from pyspark.sql import SparkSession

spark = (
SparkSession.builder.appName("JupyterApp")
.master("k8s://https://kubernetes.default.svc:443")
.config("spark.submit.deployMode", "client")
.config("spark.executor.instances", "1")
.config("spark.executor.memory", "1G")
.config("spark.driver.memory", "1G")
.config("spark.executor.cores", "1")
.config("spark.kubernetes.namespace", "spark-operator")
.config(
"spark.kubernetes.container.image", "spark:3.5.0"
)
.config("spark.kubernetes.authenticate.driver.serviceAccountName", "spark-operator")
.getOrCreate()
)

```

The executors are being creating and killed right after they are created. **Cannot see driver pod anywhere**.
The error we get with the above is given below;

```
Py4JJavaError: An error occurred while calling None.org.apache.spark.api.java.JavaSparkContext.
: java.lang.IllegalStateException: Spark context stopped while waiting for backend
at org.apache.spark.scheduler.TaskSchedulerImpl.waitBackendReady(TaskSchedulerImpl.scala:1224)
at org.apache.spark.scheduler.TaskSchedulerImpl.postStartHook(TaskSchedulerImpl.scala:246)
at org.apache.spark.SparkContext.(SparkContext.scala:694)
at org.apache.spark.api.java.JavaSparkContext.(JavaSparkContext.scala:58)
at java.base/jdk.internal.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at java.base/jdk.internal.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:77)
at java.base/jdk.internal.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.base/java.lang.reflect.Constructor.newInstanceWithCaller(Constructor.java:499)
at java.base/java.lang.reflect.Constructor.newInstance(Constructor.java:480)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:247)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:374)
at py4j.Gateway.invoke(Gateway.java:238)
at py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80)
at py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69)
at py4j.ClientServerConnection.waitForCommands(ClientServerConnection.java:182)
at py4j.ClientServerConnection.run(ClientServerConnection.java:106)
at java.base/java.lang.Thread.run(Thread.java:833)
```

Can anyone please help in this regard?

#### Provide a link to the example/module related to the question

#### Additional context

Start with the posted PySpark SparkSession configuration and compare the Kubernetes namespace, service account, client deploy mode, and container image settings with the observed executor and driver-pod behavior. Reproduce the failure and inspect the resulting pod and Spark context errors; the issue is complete only when the driver and executors remain available and the SparkSession starts successfully.

Written by the indexing model from the issue text.