Secure downlink transmission in UAV-assisted reconfigurable intelligent surface (RIS)-enabled multiuser MISO systems is challenging due to imperfect channel state information (CSI), untrusted energy-harvesting receivers (UEHRs), and the strong coupling among UAV deployment, transmit power control, and RIS configuration. In this paper, we study a secure UAV-assisted RIS-enabled multiuser MISO system with UEHRs, where a hovering UAV-mounted RIS is jointly optimized in terms of its location, transmit power allocation, and discrete RIS phase shifts. The objective is to maximize the worst-case secrecy energy efficiency (WCSEE) under imperfect CSI and practical discrete phase-shift constraints. The resulting problem is highly nonconvex due to the fractional objective, coupled design variables, discrete phase shifts, and CSI uncertainty. To address these challenges, we propose two complementary approaches. First, a block coordinate descent (BCD) framework combined with successive convex approximation (SCA) is developed to solve a secrecy energy efficiency (SEE) formulation, serving as a structured model-based benchmark. Second, for the more general WCSEE problem, we propose a tailored soft actor-critic (SAC) framework that captures the coupling among variables and avoids repeated iterative optimization. Simulation results show that the proposed SAC method consistently outperforms conventional optimization and deep reinforcement learning (DRL)-based benchmarks, including deep deterministic policy gradient (DDPG) and twin delayed deep deterministic policy gradient (TD3), while maintaining robustness to CSI uncertainty and stable performance across system configurations.