**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
timer = Stats.timer('scheduler.critical_section_duration')
                timer.start()
                
                num_queued_tis = self._critical_section_execute_task_instances(session=session)
                
                
                timer.stop(send=True)
            except OperationalError as e:
                timer.stop(send=False)
                if is_lock_not_available_error(error=e):
                    self.log.debug("Critical section lock held by another Scheduler")
                    Stats.incr('scheduler.critical_section_busy')
                    session.rollback()
                    return 0
                raise
            guard.commit()
            return num_queued_tis
    @retry_db_transaction
    def _get_next_dagruns_to_examine(self, session):
        """Get Next DagRuns to Examine with retries"""
        return DagRun.next_dagruns_to_examine(session)
    @retry_db_transaction
    def _create_dagruns_for_dags(self, guard, session):
        """Find Dag Models needing DagRuns and Create Dag Runs with retries in case of OperationalError"""
        query = DagModel.dags_needing_dagruns(session)
        self._create_dag_runs(query.all(), session)
        
        guard.commit()
        
    def _create_dag_runs(self, dag_models: Iterable[DagModel], session: Session) -> None: