Fully autonomous, zero-touch systems emphasizingon energy efficiency, high reliability, and ultra-low latency willbe possible with the introduction of 6G networks. But withmore devices and services, energy usage is expected to rise,necessitating sustainable solutions. A Decision Engine (DE) basedon Hierarchical Reinforcement Learning (HRL) is presented inthis research to improve the deployment of Service FunctionChains (SFCs) based on Cloud-Native Functions (CNF) in dynamiccontexts. The goal of the framework is to lower energyconsumption while improving scalability and flexibility in thecloud, far-edge, and edge domains. By simulating actual 6Gsituations, we demonstrate that the HRL-based DE improvesresource allocation, reduces latency by 80%, and considerablyreduces energy usage by 60% compared to the flat RL. Byassisting in self-optimizing network management, our methodpresents a viable route to intelligent, sustainable 6G networks.