## Step 1: Define the symbolic representation of the problem
The problem involves maximizing an objective function subject to several constraints. The variables are:
- $x_0$ for milligrams of magnesium
- $x_1$ for milligrams of vitamin B2
- $x_2$ for milligrams of vitamin C
- $x_3$ for milligrams of vitamin B6

The objective function to maximize is: $5.28x_0 + 4.71x_1 + 8.96x_2 + 6.81x_3$

## Step 2: List the constraints
The constraints given are:
- $4.9x_0 \leq 235$
- $5.9x_0 \leq 196$
- $4.36x_1 \leq 235$
- $3.03x_1 \leq 196$
- $4.7x_2 \leq 235$
- $4.5x_2 \leq 196$
- $5.86x_3 \leq 235$
- $1.5x_3 \leq 196$
- $4.9x_0 + 4.7x_2 \geq 37$
- $4.36x_1 + 4.7x_2 \geq 26$
- $3.03x_1 + 4.5x_2 \geq 36$
- $4.9x_0 + 4.36x_1 \leq 101$
- $4.9x_0 + 5.86x_3 \leq 96$
- $4.7x_2 + 5.86x_3 \leq 69$
- $4.36x_1 + 5.86x_3 \leq 141$
- $4.9x_0 + 4.7x_2 + 5.86x_3 \leq 185$
- $4.9x_0 + 4.36x_1 + 5.86x_3 \leq 100$
- $4.36x_1 + 4.7x_2 + 5.86x_3 \leq 157$
- $4.9x_0 + 4.36x_1 + 4.7x_2 + 5.86x_3 \leq 157$
- $5.9x_0 + 1.5x_3 \leq 71$
- $4.5x_2 + 1.5x_3 \leq 71$
- $3.03x_1 + 1.5x_3 \leq 186$
- $5.9x_0 + 3.03x_1 \leq 188$
- $3.03x_1 + 4.5x_2 \leq 87$
- $5.9x_0 + 4.5x_2 \leq 164$
- $5.9x_0 + 3.03x_1 + 1.5x_3 \leq 157$
- $5.9x_0 + 3.03x_1 + 4.5x_2 \leq 150$
- $5.9x_0 + 3.03x_1 + 4.5x_2 + 1.5x_3 \leq 150$

## Step 3: Convert the problem into Gurobi code
We will use the Gurobi Python API to model and solve this optimization problem.

```python
import gurobi as gp

# Define the model
model = gp.Model("optimization_problem")

# Define the variables
x0 = model.addVar(name="x0", lb=0)  # milligrams of magnesium
x1 = model.addVar(name="x1", lb=0)  # milligrams of vitamin B2
x2 = model.addVar(name="x2", lb=0)  # milligrams of vitamin C
x3 = model.addVar(name="x3", lb=0)  # milligrams of vitamin B6

# Define the objective function
model.setObjective(5.28*x0 + 4.71*x1 + 8.96*x2 + 6.81*x3, gp.GRB.MAXIMIZE)

# Add constraints
model.addConstr(4.9*x0 <= 235)
model.addConstr(5.9*x0 <= 196)
model.addConstr(4.36*x1 <= 235)
model.addConstr(3.03*x1 <= 196)
model.addConstr(4.7*x2 <= 235)
model.addConstr(4.5*x2 <= 196)
model.addConstr(5.86*x3 <= 235)
model.addConstr(1.5*x3 <= 196)

model.addConstr(4.9*x0 + 4.7*x2 >= 37)
model.addConstr(4.36*x1 + 4.7*x2 >= 26)
model.addConstr(3.03*x1 + 4.5*x2 >= 36)

model.addConstr(4.9*x0 + 4.36*x1 <= 101)
model.addConstr(4.9*x0 + 5.86*x3 <= 96)
model.addConstr(4.7*x2 + 5.86*x3 <= 69)
model.addConstr(4.36*x1 + 5.86*x3 <= 141)
model.addConstr(4.9*x0 + 4.7*x2 + 5.86*x3 <= 185)
model.addConstr(4.9*x0 + 4.36*x1 + 5.86*x3 <= 100)
model.addConstr(4.36*x1 + 4.7*x2 + 5.86*x3 <= 157)
model.addConstr(4.9*x0 + 4.36*x1 + 4.7*x2 + 5.86*x3 <= 157)

model.addConstr(5.9*x0 + 1.5*x3 <= 71)
model.addConstr(4.5*x2 + 1.5*x3 <= 71)
model.addConstr(3.03*x1 + 1.5*x3 <= 186)
model.addConstr(5.9*x0 + 3.03*x1 <= 188)
model.addConstr(3.03*x1 + 4.5*x2 <= 87)
model.addConstr(5.9*x0 + 4.5*x2 <= 164)
model.addConstr(5.9*x0 + 3.03*x1 + 1.5*x3 <= 157)
model.addConstr(5.9*x0 + 3.03*x1 + 4.5*x2 <= 150)
model.addConstr(5.9*x0 + 3.03*x1 + 4.5*x2 + 1.5*x3 <= 150)

# Solve the model
model.optimize()

# Print the solution
if model.status == gp.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
The symbolic representation of the problem is as follows:

```json
{
    'sym_variables': [
        ('x0', 'milligrams of magnesium'),
        ('x1', 'milligrams of vitamin B2'),
        ('x2', 'milligrams of vitamin C'),
        ('x3', 'milligrams of vitamin B6')
    ],
    'objective_function': '5.28*x0 + 4.71*x1 + 8.96*x2 + 6.81*x3',
    'constraints': [
        '4.9*x0 <= 235',
        '5.9*x0 <= 196',
        '4.36*x1 <= 235',
        '3.03*x1 <= 196',
        '4.7*x2 <= 235',
        '4.5*x2 <= 196',
        '5.86*x3 <= 235',
        '1.5*x3 <= 196',
        '4.9*x0 + 4.7*x2 >= 37',
        '4.36*x1 + 4.7*x2 >= 26',
        '3.03*x1 + 4.5*x2 >= 36',
        '4.9*x0 + 4.36*x1 <= 101',
        '4.9*x0 + 5.86*x3 <= 96',
        '4.7*x2 + 5.86*x3 <= 69',
        '4.36*x1 + 5.86*x3 <= 141',
        '4.9*x0 + 4.7*x2 + 5.86*x3 <= 185',
        '4.9*x0 + 4.36*x1 + 5.86*x3 <= 100',
        '4.36*x1 + 4.7*x2 + 5.86*x3 <= 157',
        '4.9*x0 + 4.36*x1 + 4.7*x2 + 5.86*x3 <= 157',
        '5.9*x0 + 1.5*x3 <= 71',
        '4.5*x2 + 1.5*x3 <= 71',
        '3.03*x1 + 1.5*x3 <= 186',
        '5.9*x0 + 3.03*x1 <= 188',
        '3.03*x1 + 4.5*x2 <= 87',
        '5.9*x0 + 4.5*x2 <= 164',
        '5.9*x0 + 3.03*x1 + 1.5*x3 <= 157',
        '5.9*x0 + 3.03*x1 + 4.5*x2 <= 150',
        '5.9*x0 + 3.03*x1 + 4.5*x2 + 1.5*x3 <= 150'
    ]
}
```