With the explosive growth of data traffic and the ubiquitous connectivity of wireless devices, the energy demands of wireless networks have inevitably escalated. Reconfigurable intelligent surface (RIS) has emerged as a promising solution for 6G networks due to its energy efficiency (EE) and low cost, while cell-free massive multiple-input multiple-output (CF-mMIMO) was proposed as an innovative network architecture without fixed cell boundaries to enhance these measures even further. However, existing studies often assume consistently high traffic loads, neglecting the dynamic nature of user demand. This can result in underutilized access points (APs) and unnecessary energy expenditure during low-demand periods. To tackle the challenge of EE in CF-mMIMO systems during low load periods, this paper proposes a novel energy-efficient transmission scheme that jointly coordinates active APs and multiple passive RISs. Specifically, a dynamic AP sleep-mode strategy is designed, where certain APs are selectively deactivated while nearby RISs assist in maintaining coverage. We formulate the EE maximization objective as a fractional programming problem and adopt the Dinkelbach method in conjunction with alternating optimization (AO) to iteratively solve the three coupled subproblems: (i) AP selection via a hybrid branch-and-bound (BnB) and greedy algorithm, (ii) transmit power optimization using a sequential convex approximation (SCA) method, initialized by a heuristic zero-forcing strategy, and (iii) RIS phase shift optimization using gradient projection. Simulation results show that the proposed scheme achieves significantly higher EE than existing methods in both low and moderate user scenarios.