**Apache Airflow version**: 2.0.0a2

**Kubernetes version (if you are using kubernetes)** (use `kubectl version`): v1.15.12

**Environment**:
- **OS** (e.g. from /etc/os-release): CentOS 7


**What happened**:

When using KubernetesExecutor, a single SIGTERM doesn't result in the scheduler actually exiting cleanly. However, it will exit after receiving a second SIGTERM.

The same behavior happens if you have an exception in the scheduler. An easy way to replicate it is to raise in `pod_mutation_hook`.

In practice, this means the scheduler doesn't get restarted by K8s after encountering a exception. Or, when terminating the scheduler pod, it sticks around until k8s ultimately sends it a SIGKILL after its grace period is up.

**What you expected to happen**:

The scheduler to exit after a single SIGTERM.

**How to reproduce it**:
The easiest way to reproduce it is to simply send a SIGKILL to your KubernetesExecutor scheduler pod:

```
$ kubectl exec {scheduler_pod_name} kill 1
```

Note, the pod stays running, while the logs report:

```
[2020-10-30 17:34:09,581] {scheduler_job.py:794} INFO - Exiting gracefully upon receiving signal 15
[2020-10-30 17:34:10,586] {process_utils.py:97} INFO - Sending Signals.SIGTERM to GPID 59
[2020-10-30 17:34:10,879] {process_utils.py:63} INFO - Process psutil.Process(pid=59, status='terminated', exitcode=0, started=
'17:33:23') (59) terminated with exit code 0
[2020-10-30 17:34:10,882] {process_utils.py:97} INFO - Sending Signals.SIGTERM to GPID 59
[2020-10-30 17:34:10,882] {scheduler_job.py:1329} INFO - Exited execute loop
```

Issue another `kill`:
```
$ kubectl exec {scheduler_pod_name} kill 1
```

And the pod will be restarted by k8s.

If you can't reproduce it, let me know and I'll try and get something running on kind.

**Anything else we need to know**:

The same behavior happens if you have an exception in the scheduler. An easy way to replicate it is to raise in a `pod_mutation_hook`.


airflow/jobs/scheduler_job.py
self.log.info("Processing file %s for tasks to queue", file_path)
        try:
            dagbag = DagBag(file_path, include_examples=False, include_smart_sensor=False)
        except Exception:  
            self.log.exception("Failed at reloading the DAG file %s", file_path)
            Stats.incr('dag_file_refresh_error', 1, 1)
            return 0, 0
        if len(dagbag.dags) > 0:
            self.log.info("DAG(s) %s retrieved from %s", dagbag.dags.keys(), file_path)
        else:
            self.log.warning("No viable dags retrieved from %s", file_path)
            self.update_import_errors(session, dagbag)
            return 0, len(dagbag.import_errors)
        self.execute_callbacks(dagbag, callback_requests)
        
        dagbag.sync_to_db()
        if pickle_dags:
            paused_dag_ids = DagModel.get_paused_dag_ids(dag_ids=dagbag.dag_ids)
            unpaused_dags: List[DAG] = [
                dag for dag_id, dag in dagbag.dags.items() if dag_id not in paused_dag_ids
            ]
            for dag in unpaused_dags:
                dag.pickle(session)
        
        try:
            self.update_import_errors(session, dagbag)
        except Exception:  
            self.log.exception("Error logging import errors!")
        return len(dagbag.dags), len(dagbag.import_errors)
class SchedulerJob(BaseJob):