6G networks aim to further improve QoS while optimizing environmental sustainability metrics, and integrate increased levels of intelligence for automated control and performance optimization. However, directly applying AI/ML-driven decisions can pose risks if the models are not adequately trained. Additionally, balancing performance with energy efficiency is essential due to the high energy demands of complex AI/ML models. Finally, the use of AI/ML introduces additional vulnerabilities that must be mitigated in addition to the need for explainable decisions to ensure trustworthiness. This article aims to address these aspects by jointly integrating intelligence, energy efficiency, and trustworthiness into M&O for 6G. The proposed framework combines AI/ML-based solutions for dynamic network and service orchestration that optimize performance and energy metrics, and Network Digital Twin management to support AI/ML processes. Additionally, the framework introduces distributed and sustainable MLOps to monitor and further minimize the energy consumption of AI/ ML processes, as well as security and explainability mechanisms to enhance the system's trustworthiness. Evaluations of the framework's components demonstrate its feasibility and performance.