To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables and formulating the objective function along with the constraints.

Let's denote:
- \(x_1\) as the number of cans of energy drink R,
- \(x_2\) as the number of cans of energy drink M.

The objective is to minimize the cost, which can be represented by the objective function: \(4x_1 + 7x_2\).

The constraints based on the problem description are:
1. The man needs at least 30 units of caffeine per day. Given that one can of R contains 2 units of caffeine and one can of M contains 5 units, we have: \(2x_1 + 5x_2 \geq 30\).
2. The man needs at least 50 units of water per day. Since one can of R contains 5 units of water and one can of M contains 8 units, we get: \(5x_1 + 8x_2 \geq 50\).

Additionally, since the number of cans cannot be negative, we have:
3. \(x_1 \geq 0\) and \(x_2 \geq 0\).

Now, let's put this into the required symbolic representation format:

```json
{
    'sym_variables': [('x1', 'number of cans of energy drink R'), ('x2', 'number of 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']
}
```

Next, we'll write the Gurobi code in Python to solve this optimization problem:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="x1", lb=0)
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="x2", lb=0)

# Set the objective function
m.setObjective(4*x1 + 7*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(2*x1 + 5*x2 >= 30, "caffeine_constraint")
m.addConstr(5*x1 + 8*x2 >= 50, "water_constraint")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Number of cans of energy drink R: {x1.x}")
    print(f"Number of cans of energy drink M: {x2.x}")
    print(f"Total cost: {m.objVal}")
else:
    print("No optimal solution found")
```