**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_task_handler.py
if try_number is None:
            next_try = task_instance.next_try_number
            try_numbers = list(range(1, next_try))
        elif try_number < 1:
            logs = [
                [('default_host', f'Error fetching the logs. Try number {try_number} is invalid.')],
            ]
            return logs, [{'end_of_log': True}]
        else:
            try_numbers = [try_number]
        logs = [''] * len(try_numbers)
        metadata_array = [{}] * len(try_numbers)
        for i, try_number_element in enumerate(try_numbers):
            log, metadata = self._read(task_instance, try_number_element, metadata)
            
            
            logs[i] = log if self._read_grouped_logs() else [(task_instance.hostname, log)]
            metadata_array[i] = metadata
        return logs, metadata_array
    def _init_file(self, ti):
        """
        Create log directory and give it correct permissions.
        :param ti: task instance object
        :return: relative log path of the given task instance
        """