## Step 1: Define the symbolic representation of the problem
The problem involves maximizing an objective function subject to several constraints. The variables are:
- $x_0$: milligrams of calcium
- $x_1$: milligrams of magnesium
- $x_2$: milligrams of vitamin B7
- $x_3$: milligrams of vitamin B2

The objective function to maximize is: $5x_0 + 8x_1 + 7x_2 + 6x_3$

## Step 2: List the constraints
The constraints given are:
1. $25x_0 \leq 162$
2. $26x_0 \leq 551$
3. $3x_0 \leq 557$
4. $6x_1 \leq 162$
5. $20x_1 \leq 551$
6. $22x_1 \leq 557$
7. $16x_2 \leq 162$
8. $26x_2 \leq 551$
9. $19x_2 \leq 557$
10. $8x_3 \leq 162$
11. $24x_3 \leq 551$
12. $18x_3 \leq 557$
13. $25x_0 + 16x_2 \geq 22$
14. $25x_0 + 6x_1 \geq 18$
15. $6x_1 + 16x_2 \geq 14$
16. $26x_0 + 20x_1 + 26x_2 \geq 107$
17. $3x_0 + 22x_1 \geq 80$
18. $22x_1 + 19x_2 + 18x_3 \geq 98$
19. $3x_0 + 22x_1 + 18x_3 \geq 98$
20. $22x_1 + 19x_2 + 18x_3 \geq 104$
21. $3x_0 + 22x_1 + 18x_3 \geq 104$
22. $6x_1 + 8x_3 \leq 102$
23. $25x_0 + 8x_3 \leq 149$
24. $25x_0 + 16x_2 + 8x_3 \leq 43$
25. $25x_0 + 6x_1 + 16x_2 \leq 84$
26. $25x_0 + 6x_1 + 8x_3 \leq 96$
27. $25x_0 + 6x_1 + 16x_2 + 8x_3 \leq 96$
28. $20x_1 + 24x_3 \leq 276$
29. $20x_1 + 26x_2 \leq 241$
30. $20x_1 + 26x_2 + 24x_3 \leq 155$
31. $26x_0 + 20x_1 + 24x_3 \leq 548$
32. $26x_0 + 20x_1 + 26x_2 \leq 329$
33. $26x_0 + 26x_2 + 24x_3 \leq 455$
34. $26x_0 + 20x_1 + 26x_2 + 24x_3 \leq 455$
35. $3x_0 + 19x_2 \leq 495$
36. $3x_0 + 22x_1 \leq 279$
37. $22x_1 + 19x_2 + 18x_3 \leq 513$
38. $3x_0 + 19x_2 + 18x_3 \leq 342$
39. $3x_0 + 22x_1 + 19x_2 \leq 201$
40. $3x_0 + 22x_1 + 19x_2 + 18x_3 \leq 201$

## Step 3: Convert the problem into Gurobi code
```python
import gurobi

# Define the model
model = gurobi.Model()

# Define the variables
x0 = model.addVar(name="x0", lb=0)  # milligrams of calcium
x1 = model.addVar(name="x1", lb=0)  # milligrams of magnesium
x2 = model.addVar(name="x2", lb=0)  # milligrams of vitamin B7
x3 = model.addVar(name="x3", lb=0)  # milligrams of vitamin B2

# Define the objective function
model.setObjective(5 * x0 + 8 * x1 + 7 * x2 + 6 * x3, gurobi.GRB.MAXIMIZE)

# Add constraints
model.addConstr(25 * x0 <= 162)
model.addConstr(26 * x0 <= 551)
model.addConstr(3 * x0 <= 557)
model.addConstr(6 * x1 <= 162)
model.addConstr(20 * x1 <= 551)
model.addConstr(22 * x1 <= 557)
model.addConstr(16 * x2 <= 162)
model.addConstr(26 * x2 <= 551)
model.addConstr(19 * x2 <= 557)
model.addConstr(8 * x3 <= 162)
model.addConstr(24 * x3 <= 551)
model.addConstr(18 * x3 <= 557)
model.addConstr(25 * x0 + 16 * x2 >= 22)
model.addConstr(25 * x0 + 6 * x1 >= 18)
model.addConstr(6 * x1 + 16 * x2 >= 14)
model.addConstr(26 * x0 + 20 * x1 + 26 * x2 >= 107)
model.addConstr(3 * x0 + 22 * x1 >= 80)
model.addConstr(22 * x1 + 19 * x2 + 18 * x3 >= 98)
model.addConstr(3 * x0 + 22 * x1 + 18 * x3 >= 98)
model.addConstr(22 * x1 + 19 * x2 + 18 * x3 >= 104)
model.addConstr(3 * x0 + 22 * x1 + 18 * x3 >= 104)
model.addConstr(6 * x1 + 8 * x3 <= 102)
model.addConstr(25 * x0 + 8 * x3 <= 149)
model.addConstr(25 * x0 + 16 * x2 + 8 * x3 <= 43)
model.addConstr(25 * x0 + 6 * x1 + 16 * x2 <= 84)
model.addConstr(25 * x0 + 6 * x1 + 8 * x3 <= 96)
model.addConstr(25 * x0 + 6 * x1 + 16 * x2 + 8 * x3 <= 96)
model.addConstr(20 * x1 + 24 * x3 <= 276)
model.addConstr(20 * x1 + 26 * x2 <= 241)
model.addConstr(20 * x1 + 26 * x2 + 24 * x3 <= 155)
model.addConstr(26 * x0 + 20 * x1 + 24 * x3 <= 548)
model.addConstr(26 * x0 + 20 * x1 + 26 * x2 <= 329)
model.addConstr(26 * x0 + 26 * x2 + 24 * x3 <= 455)
model.addConstr(26 * x0 + 20 * x1 + 26 * x2 + 24 * x3 <= 455)
model.addConstr(3 * x0 + 19 * x2 <= 495)
model.addConstr(3 * x0 + 22 * x1 <= 279)
model.addConstr(22 * x1 + 19 * x2 + 18 * x3 <= 513)
model.addConstr(3 * x0 + 19 * x2 + 18 * x3 <= 342)
model.addConstr(3 * x0 + 22 * x1 + 19 * x2 <= 201)
model.addConstr(3 * x0 + 22 * x1 + 19 * x2 + 18 * x3 <= 201)

# Solve the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", model.objVal)
    print("x0: ", x0.varValue)
    print("x1: ", x1.varValue)
    print("x2: ", x2.varValue)
    print("x3: ", x3.varValue)
else:
    print("The model is infeasible")
```

## Step 4: Symbolic representation
```json
{
    'sym_variables': [
        ('x0', 'milligrams of calcium'), 
        ('x1', 'milligrams of magnesium'), 
        ('x2', 'milligrams of vitamin B7'), 
        ('x3', 'milligrams of vitamin B2')
    ], 
    'objective_function': '5*x0 + 8*x1 + 7*x2 + 6*x3', 
    'constraints': [
        '25*x0 <= 162',
        '26*x0 <= 551',
        '3*x0 <= 557',
        '6*x1 <= 162',
        '20*x1 <= 551',
        '22*x1 <= 557',
        '16*x2 <= 162',
        '26*x2 <= 551',
        '19*x2 <= 557',
        '8*x3 <= 162',
        '24*x3 <= 551',
        '18*x3 <= 557',
        '25*x0 + 16*x2 >= 22',
        '25*x0 + 6*x1 >= 18',
        '6*x1 + 16*x2 >= 14',
        '26*x0 + 20*x1 + 26*x2 >= 107',
        '3*x0 + 22*x1 >= 80',
        '22*x1 + 19*x2 + 18*x3 >= 98',
        '3*x0 + 22*x1 + 18*x3 >= 98',
        '22*x1 + 19*x2 + 18*x3 >= 104',
        '3*x0 + 22*x1 + 18*x3 >= 104',
        '6*x1 + 8*x3 <= 102',
        '25*x0 + 8*x3 <= 149',
        '25*x0 + 16*x2 + 8*x3 <= 43',
        '25*x0 + 6*x1 + 16*x2 <= 84',
        '25*x0 + 6*x1 + 8*x3 <= 96',
        '25*x0 + 6*x1 + 16*x2 + 8*x3 <= 96',
        '20*x1 + 24*x3 <= 276',
        '20*x1 + 26*x2 <= 241',
        '20*x1 + 26*x2 + 24*x3 <= 155',
        '26*x0 + 20*x1 + 24*x3 <= 548',
        '26*x0 + 20*x1 + 26*x2 <= 329',
        '26*x0 + 26*x2 + 24*x3 <= 455',
        '26*x0 + 20*x1 + 26*x2 + 24*x3 <= 455',
        '3*x0 + 19*x2 <= 495',
        '3*x0 + 22*x1 <= 279',
        '22*x1 + 19*x2 + 18*x3 <= 513',
        '3*x0 + 19*x2 + 18*x3 <= 342',
        '3*x0 + 22*x1 + 19*x2 <= 201',
        '3*x0 + 22*x1 + 19*x2 + 18*x3 <= 201'
    ]
}
```