In this work, we focus on non-verbal vocal sounds emotion recognition (NVER). We investigate mamba-based audio foundation models (MAFMs) for the first time for NVER and hypothesize that MAFMs will outperform attention-based audio foundation models (AAFMs) for NVER by leveraging its state-space modeling to capture intrinsic emotional structures more effectively. Unlike AAFMs, which may amplify irrelevant patterns due to their attention mechanisms, MAFMs will extract more stable and context-aware representations, enabling better differentiation of subtle non-verbal emotional cues. Our experiments with state-of-the-art (SOTA) AAFMs and MAFMs validates our hypothesis. Further, motivated from related research such as speech emotion recognition, synthetic speech detection, where fusion of foundation models (FMs) have showed improved performance, we also explore fusion of FMs for NVER. To this end, we propose, RENO, that uses renyi-divergence as a novel loss function for effective alignment of the FMs. It also makes use of self-attention for better intra-representation interaction of the FMs. With RENO, through the heterogeneous fusion of MAFMs and AAFMs, we show the topmost performance in comparison to individual FMs, its fusion and also setting SOTA in comparison to previous SOTA work.