Based on the same random sample and same test statistic, will the p value remain the same even when the distribution of test statistic under null hypothesis is changed. Or is this scenario true only in case of a distribution and it's asymptotic distribution say t and normal?
The p-value depends on the sampling distribution of the statistic under the null hypothesis. If one null hypothesis implies a different distribution (i.e., with a different mean or variance) from another null hypothesis, then the p-values will change. If the standard error of the statistic depends on the distribution under the null hypothesis, then the p-value, which depends on the standard error, will differ.
Your question has a lot to do with the subtle nature of what a p-value actually is and how it is interpreted. It's not exactly correct to say that the null hypothesis has a distribution at all; as @Noah correctly points out, the p-value relies on the sampling distribution of the test statistic: in frequentist inference, the sampling distribution is constructed partly from a hypothetical, user-supplied "null hypothesis" (such as 'the true mean $\mu=2$' in a one-sample test or '$\mu_1 - \mu_2 = 0$' in a two-sample test) that involves a statement concerning one or more of the parameters of a probability distribution. Other parameters needed to fully specify the distribution (such as the variance $\sigma^2$ in a model where normality is assumed) might also be hypothesized by the investigator, or they may be estimated using the observed data. Most likely, a technique such as maximum likelihood estimation is used to fix the distributional parameters at their "most likely" values.
What this means in regards to your question is that the p-value relies on a distribution which is partly assumed by the researcher, partly determined by the nature of the researcher's hypothesis, and partly estimated from the data. The p-value may not stay the same if either the researcher's basic assumptions or hypotheses change.
In general, however, formulating a null hypothesis is not an arbitrary process and many statistical tests are robust to mild deviations from normality (which is a common assumption). It is hard to imagine a scenario where a well-formed hypothesis changes within an experiment or a researcher makes an assumption such as normality without first having some evidence that this is a safe assumption to make.
With this in mind, I generally think of frequentist inference as being fully determined by the observed data, while knowing that this isn't exactly true. However, as long as a researcher takes the time to understand their hypotheses and assumptions, there should be no need to change them. IMHO it is quite a slippery slope to think of p-values as things that can be manipulated with a cleverly-formulated null hypothesis or a subtle assumption.