## Step 1: Define the symbolic representation of the variables
The variables are ['milligrams of vitamin B3', 'grams of protein', 'milligrams of vitamin B7', 'milligrams of magnesium']. Let's denote them as $x_1$, $x_2$, $x_3$, and $x_4$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_1$ : milligrams of vitamin B3
- $x_2$ : grams of protein
- $x_3$ : milligrams of vitamin B7
- $x_4$ : milligrams of magnesium

## 3: Define the objective function in symbolic notation
The objective function to minimize is $4x_1 + x_2 + 8x_3 + 3x_4$.

## 4: List the constraints in symbolic notation
The constraints are:
- $30x_1 \leq 236$
- $6x_1 \leq 231$
- $8x_2 \leq 236$
- $20x_2 \leq 231$
- $16x_3 \leq 236$
- $12x_3 \leq 231$
- $21x_4 \leq 236$
- $16x_4 \leq 231$
- $8x_2 + 16x_3 \geq 34$
- $30x_1 + 16x_3 \geq 24$
- $16x_3 + 21x_4 \geq 54$
- $8x_2 + 21x_4 \geq 34$
- $30x_1 + 8x_2 + 16x_3 + 21x_4 \geq 34$
- $20x_2 + 12x_3 \geq 41$
- $12x_3 + 16x_4 \geq 25$
- $6x_1 + 12x_3 \geq 50$
- $6x_1 + 16x_4 \geq 57$
- $20x_2 + 16x_4 \geq 22$
- $6x_1 + 20x_2 + 12x_3 \geq 34$
- $6x_1 + 20x_2 + 16x_4 \geq 34$
- $20x_2 + 12x_3 + 16x_4 \geq 34$
- $6x_1 + 20x_2 + 12x_3 \geq 39$
- $6x_1 + 20x_2 + 16x_4 \geq 39$
- $20x_2 + 12x_3 + 16x_4 \geq 39$
- $6x_1 + 20x_2 + 12x_3 \geq 43$
- $6x_1 + 20x_2 + 16x_4 \geq 43$
- $20x_2 + 12x_3 + 16x_4 \geq 43$
- $6x_1 + 20x_2 + 12x_3 + 16x_4 \geq 43$
- $2x_2 - 7x_4 \geq 0$
- $-9x_1 + x_4 \geq 0$
- $30x_1 + 21x_4 \leq 168$
- $8x_2 + 21x_4 \leq 71$
- $8x_2 + 16x_3 \leq 74$
- $16x_3 + 21x_4 \leq 113$
- $6x_1 + 20x_2 + 16x_4 \leq 96$
- $6x_1 + 20x_2 + 12x_3 \leq 197$
- $6x_1 + 12x_3 + 16x_4 \leq 187$

## 5: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x1', 'milligrams of vitamin B3'), 
        ('x2', 'grams of protein'), 
        ('x3', 'milligrams of vitamin B7'), 
        ('x4', 'milligrams of magnesium')
    ], 
    'objective_function': '4*x1 + x2 + 8*x3 + 3*x4', 
    'constraints': [
        '30*x1 <= 236',
        '6*x1 <= 231',
        '8*x2 <= 236',
        '20*x2 <= 231',
        '16*x3 <= 236',
        '12*x3 <= 231',
        '21*x4 <= 236',
        '16*x4 <= 231',
        '8*x2 + 16*x3 >= 34',
        '30*x1 + 16*x3 >= 24',
        '16*x3 + 21*x4 >= 54',
        '8*x2 + 21*x4 >= 34',
        '30*x1 + 8*x2 + 16*x3 + 21*x4 >= 34',
        '20*x2 + 12*x3 >= 41',
        '12*x3 + 16*x4 >= 25',
        '6*x1 + 12*x3 >= 50',
        '6*x1 + 16*x4 >= 57',
        '20*x2 + 16*x4 >= 22',
        '6*x1 + 20*x2 + 12*x3 >= 34',
        '6*x1 + 20*x2 + 16*x4 >= 34',
        '20*x2 + 12*x3 + 16*x4 >= 34',
        '6*x1 + 20*x2 + 12*x3 >= 39',
        '6*x1 + 20*x2 + 16*x4 >= 39',
        '20*x2 + 12*x3 + 16*x4 >= 39',
        '6*x1 + 20*x2 + 12*x3 >= 43',
        '6*x1 + 20*x2 + 16*x4 >= 43',
        '20*x2 + 12*x3 + 16*x4 >= 43',
        '6*x1 + 20*x2 + 12*x3 + 16*x4 >= 43',
        '2*x2 - 7*x4 >= 0',
        '-9*x1 + x4 >= 0',
        '30*x1 + 21*x4 <= 168',
        '8*x2 + 21*x4 <= 71',
        '8*x2 + 16*x3 <= 74',
        '16*x3 + 21*x4 <= 113',
        '6*x1 + 20*x2 + 16*x4 <= 96',
        '6*x1 + 20*x2 + 12*x3 <= 197',
        '6*x1 + 12*x3 + 16*x4 <= 187'
    ]
}
```

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

def optimize_problem():
    model = gurobi.Model()

    # Define variables
    x1 = model.addVar(name="x1", lb=0)  # milligrams of vitamin B3
    x2 = model.addVar(name="x2", lb=0)  # grams of protein
    x3 = model.addVar(name="x3", lb=0)  # milligrams of vitamin B7
    x4 = model.addVar(name="x4", lb=0)  # milligrams of magnesium

    # Objective function
    model.setObjective(4 * x1 + x2 + 8 * x3 + 3 * x4, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(30 * x1 <= 236)
    model.addConstr(6 * x1 <= 231)
    model.addConstr(8 * x2 <= 236)
    model.addConstr(20 * x2 <= 231)
    model.addConstr(16 * x3 <= 236)
    model.addConstr(12 * x3 <= 231)
    model.addConstr(21 * x4 <= 236)
    model.addConstr(16 * x4 <= 231)
    model.addConstr(8 * x2 + 16 * x3 >= 34)
    model.addConstr(30 * x1 + 16 * x3 >= 24)
    model.addConstr(16 * x3 + 21 * x4 >= 54)
    model.addConstr(8 * x2 + 21 * x4 >= 34)
    model.addConstr(30 * x1 + 8 * x2 + 16 * x3 + 21 * x4 >= 34)
    model.addConstr(20 * x2 + 12 * x3 >= 41)
    model.addConstr(12 * x3 + 16 * x4 >= 25)
    model.addConstr(6 * x1 + 12 * x3 >= 50)
    model.addConstr(6 * x1 + 16 * x4 >= 57)
    model.addConstr(20 * x2 + 16 * x4 >= 22)
    model.addConstr(6 * x1 + 20 * x2 + 12 * x3 >= 34)
    model.addConstr(6 * x1 + 20 * x2 + 16 * x4 >= 34)
    model.addConstr(20 * x2 + 12 * x3 + 16 * x4 >= 34)
    model.addConstr(6 * x1 + 20 * x2 + 12 * x3 >= 39)
    model.addConstr(6 * x1 + 20 * x2 + 16 * x4 >= 39)
    model.addConstr(20 * x2 + 12 * x3 + 16 * x4 >= 39)
    model.addConstr(6 * x1 + 20 * x2 + 12 * x3 >= 43)
    model.addConstr(6 * x1 + 20 * x2 + 16 * x4 >= 43)
    model.addConstr(20 * x2 + 12 * x3 + 16 * x4 >= 43)
    model.addConstr(6 * x1 + 20 * x2 + 12 * x3 + 16 * x4 >= 43)
    model.addConstr(2 * x2 - 7 * x4 >= 0)
    model.addConstr(-9 * x1 + x4 >= 0)
    model.addConstr(30 * x1 + 21 * x4 <= 168)
    model.addConstr(8 * x2 + 21 * x4 <= 71)
    model.addConstr(8 * x2 + 16 * x3 <= 74)
    model.addConstr(16 * x3 + 21 * x4 <= 113)
    model.addConstr(6 * x1 + 20 * x2 + 16 * x4 <= 96)
    model.addConstr(6 * x1 + 20 * x2 + 12 * x3 <= 197)
    model.addConstr(6 * x1 + 12 * x3 + 16 * x4 <= 187)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"x1: {x1.varValue}")
        print(f"x2: {x2.varValue}")
        print(f"x3: {x3.varValue}")
        print(f"x4: {x4.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

optimize_problem()
```