<p>This article investigates a dynamic energy-efficient cascaded dual-shop collaborative scheduling problem with mating operations (DECDS-M), an underexplored yet practically important domain. We first formulate the problem as a mixed-integer linear programming (MILP) model to capture its structural complexity. To address the scalability and inefficient training challenges of conventional reinforcement learning (RL) methods, we propose a deep multi-agent reinforcement learning-enhanced iterated greedy algorithm (DMRLIG). An evolutionary RL mechanism is designed, where an iterated greedy controller dynamically adjusts the reward function to balance conflicting scheduling objectives and improve agent learning efficiency. A hierarchical multi-action design enables decentralized handling of both routing and sequencing decisions across workshops. Furthermore, a heterogeneous graph neural network (HGNN) captures dynamic interactions among factories, machines, and operations, facilitating high-quality policy representation. A proximal policy optimization (PPO) structure with dual actor-critics is employed to ensure robust and intelligent hierarchical decision-making. Extensive comparisons with six composite priority rules, two deep RL methods, and one metaheuristic method demonstrate the superiority of DMRLIG in optimizing both total tardiness and energy consumption. Finally, validation in an electronic equipment manufacturing company highlights the practical value and industrial applicability of the proposed approach</p>