## Step 1: Define the symbolic representation of the variables
The variables are ['milligrams of magnesium', 'milligrams of vitamin B7', 'milligrams of vitamin B4', 'milligrams of vitamin B3']. Let's denote them as $x_0$, $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Define the symbolic representation of the problem
The objective function to maximize is $2.99x_0 + 9.81x_1 + 2.56x_2 + 7.53x_3$.

## Step 3: List all the constraints
The constraints are:
- $22x_0 \leq 121$
- $7x_0 \leq 139$
- $14x_0 \leq 263$
- $10x_1 \leq 121$
- $5x_1 \leq 139$
- $23x_1 \leq 263$
- $2x_2 \leq 121$
- $25x_2 \leq 139$
- $25x_2 \leq 263$
- $3x_3 \leq 121$
- $6x_3 \leq 139$
- $18x_3 \leq 263$
- $7x_0 + 25x_2 \geq 11$
- $25x_2 + 18x_3 \geq 22$
- $23x_1 + 18x_3 \geq 36$
- $14x_0 + 25x_2 \geq 50$
- $14x_0 + 23x_1 + 25x_2 \geq 61$
- $14x_0 + 25x_2 + 18x_3 \geq 61$
- $14x_0 + 23x_1 + 25x_2 \geq 45$
- $14x_0 + 25x_2 + 18x_3 \geq 45$
- $10x_1 + 2x_2 \leq 119$
- $22x_0 + 2x_2 \leq 101$
- $22x_0 + 3x_3 \leq 120$
- $22x_0 + 10x_1 + 2x_2 + 3x_3 \leq 120$
- $7x_0 + 5x_1 \leq 94$
- $5x_1 + 25x_2 \leq 108$
- $7x_0 + 6x_3 \leq 106$
- $5x_1 + 6x_3 \leq 74$
- $7x_0 + 25x_2 \leq 136$
- $25x_2 + 6x_3 \leq 40$
- $7x_0 + 5x_1 + 25x_2 + 6x_3 \leq 40$
- $23x_1 + 18x_3 \leq 83$
- $14x_0 + 25x_2 \leq 172$
- $14x_0 + 23x_1 \leq 182$
- $14x_0 + 18x_3 \leq 73$
- $14x_0 + 23x_1 + 25x_2 + 18x_3 \leq 73$

## Step 4: Provide the symbolic representation of the problem in JSON format
```json
{
    'sym_variables': [
        ('x0', 'milligrams of magnesium'), 
        ('x1', 'milligrams of vitamin B7'), 
        ('x2', 'milligrams of vitamin B4'), 
        ('x3', 'milligrams of vitamin B3')
    ], 
    'objective_function': '2.99*x0 + 9.81*x1 + 2.56*x2 + 7.53*x3', 
    'constraints': [
        '22*x0 <= 121',
        '7*x0 <= 139',
        '14*x0 <= 263',
        '10*x1 <= 121',
        '5*x1 <= 139',
        '23*x1 <= 263',
        '2*x2 <= 121',
        '25*x2 <= 139',
        '25*x2 <= 263',
        '3*x3 <= 121',
        '6*x3 <= 139',
        '18*x3 <= 263',
        '7*x0 + 25*x2 >= 11',
        '25*x2 + 18*x3 >= 22',
        '23*x1 + 18*x3 >= 36',
        '14*x0 + 25*x2 >= 50',
        '14*x0 + 23*x1 + 25*x2 >= 61',
        '14*x0 + 25*x2 + 18*x3 >= 61',
        '14*x0 + 23*x1 + 25*x2 >= 45',
        '14*x0 + 25*x2 + 18*x3 >= 45',
        '10*x1 + 2*x2 <= 119',
        '22*x0 + 2*x2 <= 101',
        '22*x0 + 3*x3 <= 120',
        '22*x0 + 10*x1 + 2*x2 + 3*x3 <= 120',
        '7*x0 + 5*x1 <= 94',
        '5*x1 + 25*x2 <= 108',
        '7*x0 + 6*x3 <= 106',
        '5*x1 + 6*x3 <= 74',
        '7*x0 + 25*x2 <= 136',
        '25*x2 + 6*x3 <= 40',
        '7*x0 + 5*x1 + 25*x2 + 6*x3 <= 40',
        '23*x1 + 18*x3 <= 83',
        '14*x0 + 25*x2 <= 172',
        '14*x0 + 23*x1 <= 182',
        '14*x0 + 18*x3 <= 73',
        '14*x0 + 23*x1 + 25*x2 + 18*x3 <= 73'
    ]
}
```

## Step 5: Write the Gurobi code for the optimization problem
```python
import gurobi

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

    # Define the variables
    x0 = model.addVar(name='x0', lb=-gurobi.GRB.INFINITY)  # milligrams of magnesium
    x1 = model.addVar(name='x1', lb=-gurobi.GRB.INFINITY)  # milligrams of vitamin B7
    x2 = model.addVar(name='x2', lb=-gurobi.GRB.INFINITY)  # milligrams of vitamin B4
    x3 = model.addVar(name='x3', lb=-gurobi.GRB.INFINITY)  # milligrams of vitamin B3

    # Define the objective function
    model.setObjective(2.99 * x0 + 9.81 * x1 + 2.56 * x2 + 7.53 * x3, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(22 * x0 <= 121)
    model.addConstr(7 * x0 <= 139)
    model.addConstr(14 * x0 <= 263)
    model.addConstr(10 * x1 <= 121)
    model.addConstr(5 * x1 <= 139)
    model.addConstr(23 * x1 <= 263)
    model.addConstr(2 * x2 <= 121)
    model.addConstr(25 * x2 <= 139)
    model.addConstr(25 * x2 <= 263)
    model.addConstr(3 * x3 <= 121)
    model.addConstr(6 * x3 <= 139)
    model.addConstr(18 * x3 <= 263)
    model.addConstr(7 * x0 + 25 * x2 >= 11)
    model.addConstr(25 * x2 + 18 * x3 >= 22)
    model.addConstr(23 * x1 + 18 * x3 >= 36)
    model.addConstr(14 * x0 + 25 * x2 >= 50)
    model.addConstr(14 * x0 + 23 * x1 + 25 * x2 >= 61)
    model.addConstr(14 * x0 + 25 * x2 + 18 * x3 >= 61)
    model.addConstr(14 * x0 + 23 * x1 + 25 * x2 >= 45)
    model.addConstr(14 * x0 + 25 * x2 + 18 * x3 >= 45)
    model.addConstr(10 * x1 + 2 * x2 <= 119)
    model.addConstr(22 * x0 + 2 * x2 <= 101)
    model.addConstr(22 * x0 + 3 * x3 <= 120)
    model.addConstr(22 * x0 + 10 * x1 + 2 * x2 + 3 * x3 <= 120)
    model.addConstr(7 * x0 + 5 * x1 <= 94)
    model.addConstr(5 * x1 + 25 * x2 <= 108)
    model.addConstr(7 * x0 + 6 * x3 <= 106)
    model.addConstr(5 * x1 + 6 * x3 <= 74)
    model.addConstr(7 * x0 + 25 * x2 <= 136)
    model.addConstr(25 * x2 + 6 * x3 <= 40)
    model.addConstr(7 * x0 + 5 * x1 + 25 * x2 + 6 * x3 <= 40)
    model.addConstr(23 * x1 + 18 * x3 <= 83)
    model.addConstr(14 * x0 + 25 * x2 <= 172)
    model.addConstr(14 * x0 + 23 * x1 <= 182)
    model.addConstr(14 * x0 + 18 * x3 <= 73)
    model.addConstr(14 * x0 + 23 * x1 + 25 * x2 + 18 * x3 <= 73)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print("Milligrams of magnesium: ", x0.varValue)
        print("Milligrams of vitamin B7: ", x1.varValue)
        print("Milligrams of vitamin B4: ", x2.varValue)
        print("Milligrams of vitamin B3: ", x3.varValue)
        print("Objective function value: ", model.objVal)
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```