**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
state=State.FAILED,
            execution_date=DEFAULT_DATE + timedelta(days=1),
            start_date=DEFAULT_DATE + timedelta(days=1),
            session=session,
        )
        ti = dr.get_task_instance(task_id=op1.task_id, session=session)
        ti.state = initial_task_state
        session.commit()
        
        
        self.scheduler_job = SchedulerJob(num_runs=2, processor_poll_interval=30)
        self.scheduler_job.dagbag = dagbag
        executor = MockExecutor(do_update=False)
        executor.queued_tasks
        self.scheduler_job.executor = executor
        processor = mock.MagicMock()
        processor.done = False
        self.scheduler_job.processor_agent = processor
        with mock.patch.object(settings, "USE_JOB_SCHEDULE", False), conf_vars(
            {('scheduler', 'clean_tis_without_dagrun_interval'): '0.001'}
        ):
            self.scheduler_job._run_scheduler_loop()
        ti = dr.get_task_instance(task_id=op1.task_id, session=session)
        assert ti.state == expected_task_state
        assert ti.start_date is not None
        if expected_task_state in State.finished:
            assert ti.end_date is not None
            assert ti.start_date == ti.end_date
            assert ti.duration is not None
    def test_dagrun_timeout_verify_max_active_runs(self):