## Step 1: Define the symbolic representation of the variables
The variables are 'grams of protein', 'milligrams of vitamin B2', and 'grams of carbohydrates', which can be represented symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to maximize is $1 \times \text{grams of protein} + 6 \times \text{milligrams of vitamin B2} + 2 \times \text{grams of carbohydrates}$, which translates to $x_0 + 6x_1 + 2x_2$.

## 3: List the constraints in symbolic notation
1. $\text{Muscle growth index of } x_0 = 3.48$
2. $\text{Kidney support index of } x_0 = 3.0$
3. $\text{Energy stability index of } x_0 = 0.28$
4. $\text{Muscle growth index of } x_1 = 3.43$
5. $\text{Kidney support index of } x_1 = 3.44$
6. $\text{Energy stability index of } x_1 = 2.49$
7. $\text{Muscle growth index of } x_2 = 0.04$
8. $\text{Kidney support index of } x_2 = 3.52$
9. $\text{Energy stability index of } x_2 = 1.22$
10. $3.48x_0 + 0.04x_2 \geq 28$
11. $3.48x_0 + 3.43x_1 \geq 34$
12. $3.0x_0 + 3.44x_1 \geq 25$
13. $0.28x_0 + 2.49x_1 \geq 54$
14. $6x_1 - 5x_2 \geq 0$
15. $3.43x_1 + 0.04x_2 \leq 129$
16. $3.48x_0 + 0.04x_2 \leq 176$
17. $3.48x_0 + 3.43x_1 + 0.04x_2 \leq 176$
18. $3.44x_1 + 3.52x_2 \leq 128$
19. $3.0x_0 + 3.52x_2 \leq 61$
20. $3.0x_0 + 3.44x_1 + 3.52x_2 \leq 61$
21. $2.49x_1 + 1.22x_2 \leq 157$
22. $0.28x_0 + 2.49x_1 \leq 79$
23. $0.28x_0 + 2.49x_1 + 1.22x_2 \leq 79$

## 4: Define the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'grams of protein'), 
        ('x1', 'milligrams of vitamin B2'), 
        ('x2', 'grams of carbohydrates')
    ], 
    'objective_function': 'x0 + 6*x1 + 2*x2', 
    'constraints': [
        'x0 >= 0', 'x1 >= 0', 'x2 >= 0',
        '3.48*x0 = 3.48', 
        '3.0*x0 = 3.0', 
        '0.28*x0 = 0.28', 
        '3.43*x1 = 3.43', 
        '3.44*x1 = 3.44', 
        '2.49*x1 = 2.49', 
        '0.04*x2 = 0.04', 
        '3.52*x2 = 3.52', 
        '1.22*x2 = 1.22', 
        '3.48*x0 + 0.04*x2 >= 28', 
        '3.48*x0 + 3.43*x1 >= 34', 
        '3.0*x0 + 3.44*x1 >= 25', 
        '0.28*x0 + 2.49*x1 >= 54', 
        '6*x1 - 5*x2 >= 0', 
        '3.43*x1 + 0.04*x2 <= 129', 
        '3.48*x0 + 0.04*x2 <= 176', 
        '3.48*x0 + 3.43*x1 + 0.04*x2 <= 176', 
        '3.44*x1 + 3.52*x2 <= 128', 
        '3.0*x0 + 3.52*x2 <= 61', 
        '3.0*x0 + 3.44*x1 + 3.52*x2 <= 61', 
        '2.49*x1 + 1.22*x2 <= 157', 
        '0.28*x0 + 2.49*x1 <= 79', 
        '0.28*x0 + 2.49*x1 + 1.22*x2 <= 79'
    ]
}
```

## 5: Implement the optimization problem using Gurobi
```python
import gurobi

# Create a new model
m = gurobi.Model()

# Define the variables
x0 = m.addVar(name="x0", lb=0, ub=None)  # grams of protein
x1 = m.addVar(name="x1", lb=0, ub=None)  # milligrams of vitamin B2
x2 = m.addVar(name="x2", lb=0, ub=None)  # grams of carbohydrates

# Objective function
m.setObjective(x0 + 6 * x1 + 2 * x2, gurobi.GRB.MAXIMIZE)

# Constraints
m.addConstr(3.48 * x0 == 3.48, name="c1")
m.addConstr(3.0 * x0 == 3.0, name="c2")
m.addConstr(0.28 * x0 == 0.28, name="c3")
m.addConstr(3.43 * x1 == 3.43, name="c4")
m.addConstr(3.44 * x1 == 3.44, name="c5")
m.addConstr(2.49 * x1 == 2.49, name="c6")
m.addConstr(0.04 * x2 == 0.04, name="c7")
m.addConstr(3.52 * x2 == 3.52, name="c8")
m.addConstr(1.22 * x2 == 1.22, name="c9")
m.addConstr(3.48 * x0 + 0.04 * x2 >= 28, name="c10")
m.addConstr(3.48 * x0 + 3.43 * x1 >= 34, name="c11")
m.addConstr(3.0 * x0 + 3.44 * x1 >= 25, name="c12")
m.addConstr(0.28 * x0 + 2.49 * x1 >= 54, name="c13")
m.addConstr(6 * x1 - 5 * x2 >= 0, name="c14")
m.addConstr(3.43 * x1 + 0.04 * x2 <= 129, name="c15")
m.addConstr(3.48 * x0 + 0.04 * x2 <= 176, name="c16")
m.addConstr(3.48 * x0 + 3.43 * x1 + 0.04 * x2 <= 176, name="c17")
m.addConstr(3.44 * x1 + 3.52 * x2 <= 128, name="c18")
m.addConstr(3.0 * x0 + 3.52 * x2 <= 61, name="c19")
m.addConstr(3.0 * x0 + 3.44 * x1 + 3.52 * x2 <= 61, name="c20")
m.addConstr(2.49 * x1 + 1.22 * x2 <= 157, name="c21")
m.addConstr(0.28 * x0 + 2.49 * x1 <= 79, name="c22")
m.addConstr(0.28 * x0 + 2.49 * x1 + 1.22 * x2 <= 79, name="c23")

# Optimize the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("x0: ", x0.varValue)
    print("x1: ", x1.varValue)
    print("x2: ", x2.varValue)
else:
    print("The model is infeasible")
```