This paper proposes a cooperative reinforcement learning-based framework to jointly optimize active and passive beamforming in a passive Intelligent Reflecting Surface (PIRS)-assisted wireless communication system for green and secured communications. The framework employs two Deep Deterministic Policy Gradient (DDPG) agents: one at the Base Station (BS) for active beamforming control and the other at the PIRS for phase shift adjustments in passive beamforming. The BS agent optimizes beamforming for both Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) paths, while the PIRS agent adjusts phase shifts to improve the constructive contribution of the reflected signals. The user assesses the combined direct and reflected signals, using a secure rate (Rsec) based reward to guide the learning process of both agents. Through channel state information (CSI) from BS-PIRS, PIRS-user, and BS-user links, the agents learn coordinated actions to maximize the secure rate, boosting signal strength for the intended user and reducing eavesdropping risks. Simulations reveal that the proposed framework achieves substantial secured data rate efficiency gains with BS antenna configurations of 4, 8, and 16. However, further increases in antenna count require BS power adjustments for optimal performance. This joint optimization approach significantly improves secure rate and signal quality, positioning it as a valuable solution for next-generation wireless networks, such as 6G, that demand high data rates, enhanced security, and reliable connectivity.