## Step 1: Define the symbolic representation of the problem

Let's define the symbolic variables:
- $x_1$ represents the number of cans of energy drink R
- $x_2$ represents the number of cans of energy drink M

The objective is to minimize the cost, which can be represented as $4x_1 + 7x_2$.

The constraints based on the requirements are:
- Caffeine: $2x_1 + 5x_2 \geq 30$
- Water: $5x_1 + 8x_2 \geq 50$
- Non-negativity: $x_1 \geq 0, x_2 \geq 0$

## Step 2: Convert the problem into a Gurobi-compatible format

The symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'cans of energy drink R'), ('x2', 'cans of energy drink M')],
    'objective_function': '4*x1 + 7*x2',
    'constraints': [
        '2*x1 + 5*x2 >= 30',
        '5*x1 + 8*x2 >= 50',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Step 3: Write the Gurobi code in Python

Now, let's write the Gurobi code to solve this problem:
```python
import gurobi

def solve_energy_drink_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name="x1", lb=0, vtype=gurobi.GRB.INTEGER)  # cans of energy drink R
    x2 = model.addVar(name="x2", lb=0, vtype=gurobi.GRB.INTEGER)  # cans of energy drink M

    # Define the objective function
    model.setObjective(4 * x1 + 7 * x2, gurobi.GRB.MINIMIZE)

    # Define the constraints
    model.addConstr(2 * x1 + 5 * x2 >= 30, name="caffeine_requirement")
    model.addConstr(5 * x1 + 8 * x2 >= 50, name="water_requirement")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution: x1 = {x1.varValue}, x2 = {x2.varValue}")
        print(f"Minimum cost: {model.objVal}")
    else:
        print("No optimal solution found")

solve_energy_drink_problem()
```