**Description**

It appears that [`SparkSubmitHook`](https://airflow.apache.org/docs/stable/_api/airflow/contrib/operators/spark_submit_operator/index.html)'s [implementation](https://github.com/apache/airflow/blob/master/airflow/providers/apache/spark/hooks/spark_submit.py) of kerberos support is orthogonal to the `airflow kerberos` support. It requires us to specify a keytab in the task which will not work for us because our worker container intentionally does not have a volume mount for the keytab. This means we will have to modify SparkSubmit[Hook/Operator] to [use ticket cache](https://spark.apache.org/docs/latest/security.html#using-a-ticket-cache).

**Use case / motivation**

I want to use spark submit operator natively w/ ccache. 

**Proposed Changes **


I think this should be an addition of "use_krb5ccache" variable to hook and operator that defaults to 'False' for backwards compatibility.
When it is `True` we should add the following to the spark submit command construction:
```python3
if self.use_krb5ccache:
    if not os.getenv('KRB5CCNAME'):
        raise AirflowException("KRB5CCNAME environment variable not set while trying to us ticket from ccache.")
    connection_cmd += [
         "--conf",
         "spark.kerberos.renewal.credentials=ccache"
     ]
```

We should also fall back to use principal from security kerberos config if not specified in the task.
```
   self._principal = principal if principal else conf.get('kerberos', 'principal')
```

Note I've tested something similar via cluster policy as a workaround in my current project:
```python3
def spark_tasks_use_ccache(task: BaseOperator):
    """Configure SparkSubmitOperator tasks to use kerberos ticket cache."""
    if isinstance(task, SparkSubmitOperator):
        # pylint: disable=protected-access
        if task._conf:  # noqa
            task._conf["spark.kerberos.renewal.credentials"] = "ccache"  # noqa
        task._principal = conf.get('kerberos', 'principal')
```


cc: @mik-laj @potiuk WDYT about contributing this sort of thing back to airflow? should we change this in the hook? would it be interesting to contribute useful example cluster policies to airflow?
self._total_executor_cores = total_executor_cores
        self._executor_cores = executor_cores
        self._executor_memory = executor_memory
        self._driver_memory = driver_memory
        self._keytab = keytab
        self._principal = principal
        self._proxy_user = proxy_user
        self._name = name
        self._num_executors = num_executors
        self._status_poll_interval = status_poll_interval
        self._application_args = application_args
        self._env_vars = env_vars
        self._verbose = verbose
        self._submit_sp: Any | None = None
        self._yarn_application_id: str | None = None
        self._kubernetes_driver_pod: str | None = None
        self.spark_binary = spark_binary
        self._connection = self._resolve_connection()
        self._is_yarn = "yarn" in self._connection["master"]
        self._is_kubernetes = "k8s" in self._connection["master"]
        if self._is_kubernetes and kube_client is None:
            raise RuntimeError(
                f"{self._connection['master']} specified by kubernetes dependencies are not installed!"
            )
        self._should_track_driver_status = self._resolve_should_track_driver_status()
        self._driver_id: str | None = None
        self._driver_status: str | None = None
        self._spark_exit_code: int | None = None
        self._env: dict[str, Any] | None = None
    def _resolve_should_track_driver_status(self) -> bool: