To solve this problem, we need to define the decision variables, objective function, and constraints. Let's break down the problem:

- Decision variables: We have two main decisions - how many cans of energy drink R (let's denote this as \(R\)) and how many cans of energy drink M (denoted as \(M\)) the man should buy.
- Objective function: The goal is to minimize the total cost. Given that the cost per can of energy drink R is $4 and the cost per can of energy drink M is $7, our objective function can be written as: Minimize \(4R + 7M\).
- Constraints:
  - Caffeine requirement: The man needs at least 30 units of caffeine. Given that a can of R contains 2 units of caffeine and a can of M contains 5 units, we have the constraint \(2R + 5M \geq 30\).
  - Water requirement: He also needs at least 50 units of water. With R containing 5 units of water and M containing 8 units, we get another constraint \(5R + 8M \geq 50\).
  - Non-negativity constraints: Since the man cannot buy a negative number of cans, we have \(R \geq 0\) and \(M \geq 0\).

Let's express this problem in Gurobi code using Python:

```python
from gurobipy import *

# Create a new model
m = Model("Energy_Drink_Problem")

# Define the decision variables
R = m.addVar(vtype=GRB.CONTINUOUS, name="EnergyDrinkR", lb=0)
M = m.addVar(vtype=GRB.CONTINUOUS, name="EnergyDrinkM", lb=0)

# Set the objective function
m.setObjective(4*R + 7*M, GRB.MINIMIZE)

# Add constraints
m.addConstr(2*R + 5*M >= 30, "Caffeine_Requirement")
m.addConstr(5*R + 8*M >= 50, "Water_Requirement")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Buy {R.x} cans of Energy Drink R")
    print(f"Buy {M.x} cans of Energy Drink M")
    print(f"Total cost: ${4*R.x + 7*M.x}")
else:
    print("No optimal solution found")

```