**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
dummy2 = DummyOperator(task_id="dummy2")
        dummy3 = DummyOperator(task_id="dummy3")
        [dummy1, dummy2] >> dummy3
    
    dag.clear()
    dr = dag.create_dagrun(run_type=DagRunType.MANUAL, state=State.RUNNING, execution_date=DEFAULT_DATE)
    assert dr is not None
    with create_session() as session:
        tis = {ti.task_id: ti for ti in dr.get_task_instances(session=session)}
        
        tis[dummy1.task_id].state = State.SKIPPED
        tis[dummy2.task_id].state = State.SUCCESS
        assert tis[dummy3.task_id].state == State.NONE
    
    
    with create_session() as session:
        tis = {ti.task_id: ti for ti in dr.get_task_instances(session=session)}
        assert tis[dummy1.task_id].state == State.SKIPPED
        assert tis[dummy2.task_id].state == State.SUCCESS
        
        assert tis[dummy3.task_id].state == State.SKIPPED
@pytest.mark.quarantined
class TestSchedulerJobQueriesCount(unittest.TestCase):
    """
    These tests are designed to detect changes in the number of queries for
    different DAG files. These tests allow easy detection when a change is
    made that affects the performance of the SchedulerJob.
    """
    @staticmethod
    def clean_db():