1. Scalability: External orchestration tools like Apache Airflow are specifically designed to handle complex data pipelines with multiple interdependent tasks. They offer a scalable and efficient method to manage dependencies, ensuring tasks are executed in the correct order to minimize idle time and resource wastage.

2. Flexibility: Apache Airflow supports the definition of complex workflows with dependencies, retries, and error handling. This level of flexibility is not easily achievable within Databricks alone.

3. Monitoring and Logging: Apache Airflow provides robust monitoring and logging capabilities, offering real-time visibility into task and workflow statuses. This visibility is crucial for identifying bottlenecks, optimizing resource usage, and troubleshooting issues.

4. Integration with Databricks: Apache Airflow can seamlessly integrate with Databricks, allowing for the efficient execution of tasks on the Databricks platform while managing dependencies effectively.

5. Automation: Using Apache Airflow automates the management of task dependencies, reducing the need for manual oversight and intervention. This automation streamlines task execution, minimizes idle time, and maximizes resource utilization in complex data pipeline scenarios.

In summary, leveraging external orchestration tools like Apache Airflow is the most efficient approach for managing dependencies in a complex data pipeline scheduled via Databricks. It combines scalability, flexibility, monitoring capabilities, seamless integration, and automation to minimize idle time and resource wastage.