Hybrid-powered High Performance Computing (HPC) centers can reduce both operational costs and carbon footprints by aligning job execution with renewable supply and time-varying electricity prices. However, existing Deep Reinforcement Learning (DRL) schedulers often neglect price signals, rely on synthetic energy traces, or conflate job selection and delay, limiting their real-world applicability. This work introduces a cost-aware, multi-action DRL framework that embeds a hybrid energy cost model into an HPC scheduling environment, enabling agents to incorporate dynamic price signals and renewable forecasts while decoupling job selection from execution delay. Evaluated on production-scale workload traces and measured renewable profiles, the proposed scheduler consistently reduces energy costs, improves renewable utilization, and maintains competitive job responsiveness compared with heuristic and learning-based baselines. These results demonstrate a practical path toward economically and environmentally sustainable HPC operations.