Cell-free massive MIMO (CF mMIMO) is a promising paradigm to support ultra-reliable and ultra-low latency communications (URLLC) and boost energy efficiency (EE). However, the transceiver impairments (TIs) are inevitable in practical CF mMIMO systems, which will increase interference and degrade the system performance. In this letter, we introduce the rate-splitting multiple access (RSMA) technology to achieve energy-efficient URLLC in CF mMIMO systems with TIs. Specifically, the distortions caused by TIs are considered during channel estimation and downlink transmission using RSMA. Next, the power allocation problem to maximize the EE under URLLC targets is formulated. To solve the problem, we propose a Deep Reinforcement Learning (DRL)-based approach, namely RSMA-DRL, to optimize the power allocation strategy to common and private streams. Simulation results show that RSMA-DRL can obtain higher EE than space-division multiple access (SDMA)-DRL and Traditional approaches for the given the latency and block error rate (BLER) constraints.