**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
dr = dag.create_dagrun(
                run_type=DagRunType.SCHEDULED,
                execution_date=dag.following_schedule(dr.execution_date),
                state=State.RUNNING,
            )
        ex_date = dr.execution_date
        for tid, state in expected_task_states.items():
            if state != State.FAILED:
                continue
            self.null_exec.mock_task_fail(dag_id, tid, ex_date)
        try:
            dag = DagBag().get_dag(dag.dag_id)
            assert not isinstance(dag, SerializedDAG)
            
            dag.run(start_date=ex_date, end_date=ex_date, executor=self.null_exec, **run_kwargs)
        except AirflowException:
            pass
        
        for task_id, expected_state in expected_task_states.items():
            task = dag.get_task(task_id)
            ti = TaskInstance(task, ex_date)
            ti.refresh_from_db()
            assert ti.state == expected_state
        
        dr = DagRun.find(dag_id=dag_id, execution_date=ex_date)
        dr = dr[0]
        dr.dag = dag
        assert dr.state == dagrun_state
    def test_dagrun_fail(self):