**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 pool_slots_free == 0:
            self.log.debug("All pools are full!")
            return executable_tis
        max_tis = min(max_tis, pool_slots_free)
        
        
        
        query = (
            session.query(TI)
            .outerjoin(TI.dag_run)
            .filter(or_(DR.run_id.is_(None), DR.run_type != DagRunType.BACKFILL_JOB))
            .join(TI.dag_model)
            .filter(not_(DM.is_paused))
            .filter(TI.state == State.SCHEDULED)
            .options(selectinload('dag_model'))
        )
        starved_pools = [pool_name for pool_name, stats in pools.items() if stats['open'] <= 0]
        if starved_pools:
            query = query.filter(not_(TI.pool.in_(starved_pools)))
        query = query.limit(max_tis)
        task_instances_to_examine: List[TI] = with_row_locks(
            query,
            of=TI,
            session=session,
            **skip_locked(session=session),
        ).all()
        
        
        if len(task_instances_to_examine) == 0:
            self.log.debug("No tasks to consider for execution.")