**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/log/file_processor_handler.py
"""
        Create log file and directory if required.
        :param filename: task instance object
        :return: relative log path of the given task instance
        """
        relative_log_file_path = os.path.join(self._get_log_directory(), self._render_filename(filename))
        log_file_path = os.path.abspath(relative_log_file_path)
        directory = os.path.dirname(log_file_path)
        Path(directory).mkdir(parents=True, exist_ok=True)
        if not os.path.exists(log_file_path):
            open(log_file_path, "a").close()
        return log_file_path