**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
try:
            
            with redirect_stdout(StreamLogWriter(log, logging.INFO)), redirect_stderr(
                StreamLogWriter(log, logging.WARN)
            ), Stats.timer() as timer:
                
                settings.configure_orm()
                
                
                
                threading.current_thread().name = thread_name
                log.info("Started process (PID=%s) to work on %s", os.getpid(), file_path)
                dag_file_processor = DagFileProcessor(dag_ids=dag_ids, log=log)
                result: Tuple[int, int] = dag_file_processor.process_file(
                    file_path=file_path,
                    pickle_dags=pickle_dags,
                    callback_requests=callback_requests,
                )
                result_channel.send(result)
            log.info("Processing %s took %.3f seconds", file_path, timer.duration)
        except Exception:  
            
            log.exception("Got an exception! Propagating...")
            raise
        finally:
            
            
            settings.dispose_orm()
            result_channel.close()
    def start(self) -> None: