**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
if not tis_with_right_state:
            return len(event_buffer)
        
        filter_for_tis = TI.filter_for_tis(tis_with_right_state)
        tis: List[TI] = session.query(TI).filter(filter_for_tis).options(selectinload('dag_model')).all()
        for ti in tis:
            try_number = ti_primary_key_to_try_number_map[ti.key.primary]
            buffer_key = ti.key.with_try_number(try_number)
            state, info = event_buffer.pop(buffer_key)
            
            if state == State.QUEUED:
                ti.external_executor_id = info
                self.log.info("Setting external_id for %s to %s", ti, info)
                continue
            if ti.try_number == buffer_key.try_number and ti.state == State.QUEUED:
                Stats.incr('scheduler.tasks.killed_externally')
                msg = (
                    "Executor reports task instance %s finished (%s) although the "
                    "task says its %s. (Info: %s) Was the task killed externally?"
                )
                self.log.error(msg, ti, state, ti.state, info)
                request = TaskCallbackRequest(
                    full_filepath=ti.dag_model.fileloc,
                    simple_task_instance=SimpleTaskInstance(ti),
                    msg=msg % (ti, state, ti.state, info),
                )
                self.processor_agent.send_callback_to_execute(request)
        return len(event_buffer)
    def _execute(self) -> None: