**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
"""
    FileTaskHandler is a python log handler that handles and reads
    task instance logs. It creates and delegates log handling
    to `logging.FileHandler` after receiving task instance context.
    It reads logs from task instance's host machine.
    :param base_log_folder: Base log folder to place logs.
    :param filename_template: template filename string
    """
    def __init__(self, base_log_folder: str, filename_template: str):
        super().__init__()
        self.handler = None  
        self.local_base = base_log_folder
        self.filename_template, self.filename_jinja_template = parse_template_string(filename_template)
    def set_context(self, ti: "TaskInstance"):
        """
        Provide task_instance context to airflow task handler.
        :param ti: task instance object
        """
        local_loc = self._init_file(ti)
        self.handler = logging.FileHandler(local_loc, encoding='utf-8')
        if self.formatter:
            self.handler.setFormatter(self.formatter)
        self.handler.setLevel(self.level)
    def emit(self, record):