**Apache Airflow version**: 2.0.1
**Kubernetes version**: v1.19.7
**Executor**: KubernetesExecutor

**What happened**:

If you have a worker that gets stuck in pending forever, say with a missing volume mount, the task will stay in the queued state forever. Nothing is applying a timeout on it actually being able to start.

**What you expected to happen**:

Eventually the scheduler will notice that the worker hasn't progressed past pending after a given amount of time and will mark it as a failure.

**How to reproduce it**:
```python
from airflow import DAG
from airflow.operators.bash import BashOperator
from airflow.utils.dates import days_ago
from kubernetes.client import models as k8s


default_args = {
    "owner": "airflow",
}

with DAG(
    dag_id="pending",
    default_args=default_args,
    schedule_interval=None,
    start_date=days_ago(2),
) as dag:
    BashOperator(
        task_id="forever_pending",
        bash_command="date; sleep 30; date",
        executor_config={
            "pod_override": k8s.V1Pod(
                spec=k8s.V1PodSpec(
                    containers=[
                        k8s.V1Container(
                            name="base",
                            volume_mounts=[
                                k8s.V1VolumeMount(mount_path="/foo/", name="vol")
                            ],
                        )
                    ],
                    volumes=[
                        k8s.V1Volume(
                            name="vol",
                            persistent_volume_claim=k8s.V1PersistentVolumeClaimVolumeSource(
                                claim_name="missing"
                            ),
                        )
                    ],
                )
            ),
        },
    )
```


**Anything else we need to know**:
Related to:

* #15149 (This is reporting that these pending pods don't get deleted via "Mark Failed")
* #14556 (This handles when these pending pods get deleted and is already fixed)

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: logging.Logger = 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: