**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
"PERF_DAGS_COUNT": str(dag_count),
                "PERF_TASKS_COUNT": str(task_count),
                "PERF_START_AGO": start_ago,
                "PERF_SCHEDULE_INTERVAL": schedule_interval,
                "PERF_SHAPE": shape,
            },
        ), conf_vars(
            {
                ('scheduler', 'use_job_schedule'): 'True',
                ('core', 'store_serialized_dags'): 'True',
                
                
                
                
                ('core', 'min_serialized_dag_update_interval'): '100',
                ('core', 'min_serialized_dag_fetch_interval'): '100',
            }
        ):
            dagbag = DagBag(dag_folder=ELASTIC_DAG_FILE, include_examples=False)
            dagbag.sync_to_db()
            mock_agent = mock.MagicMock()
            self.scheduler_job = SchedulerJob(subdir=PERF_DAGS_FOLDER, num_runs=1)
            self.scheduler_job.executor = MockExecutor(do_update=False)
            self.scheduler_job.heartbeat = mock.MagicMock()
            self.scheduler_job.processor_agent = mock_agent
            for expected_query_count in expected_query_counts:
                with create_session() as session:
                    with assert_queries_count(expected_query_count):
                        self.scheduler_job._do_scheduling(session)