**Apache Airflow version**: 2.0.1

**Kubernetes version (if you are using kubernetes)** (use `kubectl version`): v1.17.4

**Environment**: Dev
- **OS** (e.g. from /etc/os-release): RHEL7

**What happened**:

After running fine for some time my airflow tasks got stuck in scheduled state with below error in Task Instance Details:
"All dependencies are met but the task instance is not running. In most cases this just means that the task will probably be scheduled soon unless: - The scheduler is down or under heavy load If this task instance does not start soon please contact your Airflow administrator for assistance."


**What you expected to happen**:

I restarted the scheduler then it started working fine. When i checked my metrics i realized the scheduler has a memory leak and over past 4 days it has reached up to 6GB of memory utilization

In version >2.0 we don't even have the run_duration config option to restart scheduler periodically to avoid this issue until it is resolved.

**How to reproduce it**:
I saw this issue in multiple dev instances of mine all running Airflow 2.0.1 on kubernetes with KubernetesExecutor. 
Below are the configs that i changed from the default config.
max_active_dag_runs_per_dag=32
parallelism=64
dag_concurrency=32
sql_Alchemy_pool_size=50
sql_Alchemy_max_overflow=30

**Anything else we need to know**:

The scheduler memory leaks occurs consistently in all instances i have been running. The memory utilization keeps growing for scheduler.

airflow/utils/cli.py
if command.startswith(f'{sensitive_field}='):
                        full_command[idx] = f'{sensitive_field}={"*" * 8}'
    metrics = {
        'sub_command': func_name,
        'start_datetime': datetime.utcnow(),
        'full_command': f'{full_command}',
        'user': getuser(),
    }
    if not isinstance(namespace, Namespace):
        raise ValueError(
            "namespace argument should be argparse.Namespace instance," f"but is {type(namespace)}"
        )
    tmp_dic = vars(namespace)
    metrics['dag_id'] = tmp_dic.get('dag_id')
    metrics['task_id'] = tmp_dic.get('task_id')
    metrics['execution_date'] = tmp_dic.get('execution_date')
    metrics['host_name'] = socket.gethostname()
    extra = json.dumps({k: metrics[k] for k in ('host_name', 'full_command')})
    log = Log(
        event=f'cli_{func_name}',
        task_instance=None,
        owner=metrics['user'],
        extra=extra,
        task_id=metrics.get('task_id'),
        dag_id=metrics.get('dag_id'),
        execution_date=metrics.get('execution_date'),
    )
    metrics['log'] = log
    return metrics
def process_subdir(subdir: Optional[str]):