Small cell densification is recognized as one of the most significant characteristics in the fifth-generation of communication systems (5G) and beyond. A substantial capacity boost can be achieved at a low cost by supplementing macro networks with numerous small cells to create ultra-dense heterogeneous networks, which can serve as the foundation for the next generation of services. In this paper, we investigate a model that accounts for the location and channel quality of an enhanced Mobile Broadband (eMBB) user as well as the locations, density, and energy levels of a large number of Internet of Things (IoT) devices. More specifically, the eMBB user is randomly distributed in the coverage area of the MBS, and given its channel gain, it adjusts its transmit power to achieve an acceptable Quality of Service (QoS). In contrast, the IoT devices are gathered around SBS and regulate their transmission power in accordance with their energy budget to minimize energy-efficient utility function. Due to the coupling, the Stackelberg-Nash differential game is initially used to model the power allocation problem, with the eMBB user playing the role of the leader and the IoT devices playing the role of the followers. Then, we use the mean-field approximation to construct a hierarchical mean-field game from which we can recover a set of equations that may be solved iteratively to provide the optimal power allocation strategies. Simulation results illustrate the optimal power allocation strategies and show the effectiveness of the proposed approach.