**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`.


tests/jobs/test_scheduler_job.py
dag_id = 'SchedulerJobTest.test_enqueue_task_instances_with_queued_state'
        task_id_1 = 'dummy'
        dag = DAG(dag_id=dag_id, start_date=DEFAULT_DATE)
        task1 = DummyOperator(dag=dag, task_id=task_id_1)
        dag = SerializedDAG.from_dict(SerializedDAG.to_dict(dag))
        self.scheduler_job = SchedulerJob(subdir=os.devnull)
        session = settings.Session()
        dag_model = DagModel(
            dag_id=dag_id,
            is_paused=False,
            concurrency=dag.concurrency,
            has_task_concurrency_limits=False,
        )
        session.add(dag_model)
        dr1 = dag.create_dagrun(
            run_type=DagRunType.SCHEDULED,
            execution_date=DEFAULT_DATE,
            state=State.RUNNING,
        )
        ti1 = TaskInstance(task1, dr1.execution_date)
        ti1.dag_model = dag_model
        session.merge(ti1)
        session.flush()
        with patch.object(BaseExecutor, 'queue_command') as mock_queue_command:
            self.scheduler_job._enqueue_task_instances_with_queued_state([ti1])
        assert mock_queue_command.called
        session.rollback()
    def test_critical_section_execute_task_instances(self):