The evolution of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) in the telecommunications industry have intensified the issues of network management at large scales. Dynamic service orchestration and adaptive resource allocation became a necessity for network operators to manage the rapid growth of users and data-intensive applications. The impact of network automation on energy consumption and overall operating costs is often overlooked. Guaranteeing strict performance constraints of Ultra-Reliable Low Latency Communication (URLLC) services while enhancing energy efficiency is one of the major critical problems of future communication networks, given the urgency to reduce carbon emissions and energy consumption. In this work, we study the problem of zero-touch Service Function Chain (SFC) orchestration for multi-domain networks, targeting the latency reduction of URLLC services while improving energy efficiency for beyond-5G networks. Specifically, we propose SCHE2MA, a Service CHain Energy-Efficient Management framework based on distributed Reinforcement Learning (RL), that can intelligently deploy SFCs with shared VNFs per se into a multi-domain network. Finally, we evaluate SCHE2MA through model validation and simulation while demonstrating its ability to jointly reduce average service latency by 103.4% and energy consumption by 17.1% compared to a centralized RL solution