**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
self,
        subdir: str = settings.DAGS_FOLDER,
        num_runs: int = conf.getint('scheduler', 'num_runs'),
        num_times_parse_dags: int = -1,
        processor_poll_interval: float = conf.getfloat('scheduler', 'processor_poll_interval'),
        do_pickle: bool = False,
        log: Any = None,
        *args,
        **kwargs,
    ):
        self.subdir = subdir
        self.num_runs = num_runs
        
        
        
        self.num_times_parse_dags = num_times_parse_dags
        self._processor_poll_interval = processor_poll_interval
        self.do_pickle = do_pickle
        super().__init__(*args, **kwargs)
        if log:
            self._log = log
        
        sql_conn: str = conf.get('core', 'sql_alchemy_conn').lower()
        self.using_sqlite = sql_conn.startswith('sqlite')
        self.using_mysql = sql_conn.startswith('mysql')
        self.max_tis_per_query: int = conf.getint('scheduler', 'max_tis_per_query')
        self.processor_agent: Optional[DagFileProcessorAgent] = None
        self.dagbag = DagBag(dag_folder=self.subdir, read_dags_from_db=True, load_op_links=False)
    def register_signals(self) -> None: