In industrial IoT, multi-motor control systems are crucial for meeting the complex demands of modern manufacturing. To guarantee the stability of control performance while minimizing the induced network costs, e.g., energy consumption, the co-design of control and network systems is essential. Moreover, multi-motor systems face the challenge of torque synchronization, where the torque deviation among motors can significantly affect system and control error, potentially leading to the damage of industrial IoT equipment. In this paper, we study the networked multi-motor control with torque synchronization in industrial IoT under stringent energy consumption requirements. We formulate a joint optimization problem to minimize the overall system costs including both the control error and energy consumption, and then further propose a novel hierarchical deep reinforcement learning (DRL) based control algorithm, namely, HDRL-NMC-TS, which solves the problem. Specifically, the optimization problem is decoupled into two sub-problems, each sub-problem is formulated as a Markov decision process, and respectively solved by a designed HDRL-based algorithm, for optimizing the sampling period and the voltage compensation value. Extensive simulations show our HDRL-NMC-TS algorithm is effective and outperforms counterparts.