How can you effectively strategize and implement the modernization of your existing on-premises data architecture to align with contemporary cloud-based solutions, specifically within the framework of Google Cloud Platform (GCP)? Given your organization's current infrastructure, which includes the utilization of Apache Hadoop clusters for processing diverse and voluminous datasets, alongside Apache Airflow for orchestrating numerous ETL pipelines, you're tasked with transitioning to a new architecture on GCP while minimizing disruptions to your existing orchestration processes. The goal is to seamlessly migrate your Hadoop workloads to the cloud while ensuring optimal efficiency, scalability, and compatibility with your current orchestration framework. What comprehensive approach would you recommend to achieve this objective, considering factors such as data replication, workload management, orchestration, and integration with GCP services?