## Step 1: Define the symbolic representation of the variables
The variables are ['grams of fat', 'milligrams of magnesium', 'grams of fiber', 'milligrams of vitamin B3'] which can be represented symbolically as $x_1, x_2, x_3, x_4$ respectively.

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

## 3: Define the objective function in symbolic notation
The objective function to minimize is $8x_1 + 3x_2 + 2x_3 + 3x_4$.

## 4: List the constraints in symbolic notation
The constraints are:
- $3.17x_1 \leq 132$
- $8.21x_1 \leq 168$
- $5.97x_1 \leq 228$
- $5.91x_2 \leq 132$
- $10.68x_2 \leq 168$
- $7.78x_2 \leq 228$
- $4.99x_3 \leq 132$
- $6.05x_3 \leq 168$
- $1.24x_3 \leq 228$
- $7.55x_4 \leq 132$
- $10.32x_4 \leq 168$
- $1.56x_4 \leq 228$
- $4.99x_3 + 7.55x_4 \geq 12$
- $3.17x_1 + 4.99x_3 \geq 11$
- $5.91x_2 + 7.55x_4 \geq 19$
- $3.17x_1 + 7.55x_4 \geq 16$
- $3.17x_1 + 5.91x_2 \geq 26$
- $3.17x_1 + 5.91x_2 + 7.55x_4 \geq 28$
- $3.17x_1 + 5.91x_2 + 4.99x_3 + 7.55x_4 \geq 28$
- $8.21x_1 + 10.32x_4 \geq 25$
- $8.21x_1 + 6.05x_3 \geq 24$
- $10.68x_2 + 10.32x_4 \geq 42$
- $10.68x_2 + 6.05x_3 \geq 35$
- $6.05x_3 + 10.32x_4 \geq 18$
- $10.68x_2 + 6.05x_3 + 10.32x_4 \geq 32$
- $8.21x_1 + 6.05x_3 + 10.32x_4 \geq 32$
- $8.21x_1 + 10.68x_2 + 10.32x_4 \geq 32$
- $10.68x_2 + 6.05x_3 + 10.32x_4 \geq 33$
- $8.21x_1 + 6.05x_3 + 10.32x_4 \geq 33$
- $8.21x_1 + 10.68x_2 + 10.32x_4 \geq 33$
- $10.68x_2 + 6.05x_3 + 10.32x_4 \geq 29$
- $8.21x_1 + 6.05x_3 + 10.32x_4 \geq 29$
- $8.21x_1 + 10.68x_2 + 10.32x_4 \geq 29$
- $8.21x_1 + 10.68x_2 + 6.05x_3 + 10.32x_4 \geq 29$
- $5.97x_1 + 1.56x_4 \geq 42$
- $7.78x_2 + 1.56x_4 \geq 57$
- $5.97x_1 + 7.78x_2 \geq 27$
- $5.97x_1 + 1.24x_3 + 1.56x_4 \geq 44$
- $5.97x_1 + 7.78x_2 + 1.24x_3 \geq 44$
- $5.97x_1 + 1.24x_3 + 1.56x_4 \geq 44$
- $5.97x_1 + 7.78x_2 + 1.24x_3 \geq 44$
- $5.97x_1 + 7.78x_2 + 1.24x_3 + 1.56x_4 \geq 44$
- $x_1 - 9x_3 \geq 0$
- $8.21x_1 + 6.05x_3 \leq 99$
- $10.68x_2 + 10.32x_4 \leq 76$
- $1.24x_3 + 1.56x_4 \leq 224$
- $7.78x_2 + 1.24x_3 + 1.56x_4 \leq 163$
- $5.97x_1 + 7.78x_2 + 1.56x_4 \leq 168$

## 5: Create the Gurobi model and variables
```python
import gurobi

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

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

# Define the symbolic representation of the problem
sym_variables = [('x1', 'grams of fat'), ('x2', 'milligrams of magnesium'), 
                 ('x3', 'grams of fiber'), ('x4', 'milligrams of vitamin B3')]
objective_function = '8*x1 + 3*x2 + 2*x3 + 3*x4'
constraints = [
    '3.17*x1 <= 132',
    '8.21*x1 <= 168',
    '5.97*x1 <= 228',
    '5.91*x2 <= 132',
    '10.68*x2 <= 168',
    '7.78*x2 <= 228',
    '4.99*x3 <= 132',
    '6.05*x3 <= 168',
    '1.24*x3 <= 228',
    '7.55*x4 <= 132',
    '10.32*x4 <= 168',
    '1.56*x4 <= 228',
    '4.99*x3 + 7.55*x4 >= 12',
    '3.17*x1 + 4.99*x3 >= 11',
    '5.91*x2 + 7.55*x4 >= 19',
    '3.17*x1 + 7.55*x4 >= 16',
    '3.17*x1 + 5.91*x2 >= 26',
    '3.17*x1 + 5.91*x2 + 7.55*x4 >= 28',
    '3.17*x1 + 5.91*x2 + 4.99*x3 + 7.55*x4 >= 28',
    '8.21*x1 + 10.32*x4 >= 25',
    '8.21*x1 + 6.05*x3 >= 24',
    '10.68*x2 + 10.32*x4 >= 42',
    '10.68*x2 + 6.05*x3 >= 35',
    '6.05*x3 + 10.32*x4 >= 18',
    '10.68*x2 + 6.05*x3 + 10.32*x4 >= 32',
    '8.21*x1 + 6.05*x3 + 10.32*x4 >= 32',
    '8.21*x1 + 10.68*x2 + 10.32*x4 >= 32',
    '10.68*x2 + 6.05*x3 + 10.32*x4 >= 33',
    '8.21*x1 + 6.05*x3 + 10.32*x4 >= 33',
    '8.21*x1 + 10.68*x2 + 10.32*x4 >= 33',
    '10.68*x2 + 6.05*x3 + 10.32*x4 >= 29',
    '8.21*x1 + 6.05*x3 + 10.32*x4 >= 29',
    '8.21*x1 + 10.68*x2 + 10.32*x4 >= 29',
    '8.21*x1 + 10.68*x2 + 6.05*x3 + 10.32*x4 >= 29',
    '5.97*x1 + 1.56*x4 >= 42',
    '7.78*x2 + 1.56*x4 >= 57',
    '5.97*x1 + 7.78*x2 >= 27',
    '5.97*x1 + 1.24*x3 + 1.56*x4 >= 44',
    '5.97*x1 + 7.78*x2 + 1.24*x3 >= 44',
    '5.97*x1 + 1.24*x3 + 1.56*x4 >= 44',
    '5.97*x1 + 7.78*x2 + 1.24*x3 >= 44',
    '5.97*x1 + 7.78*x2 + 1.24*x3 + 1.56*x4 >= 44',
    'x1 - 9*x3 >= 0',
    '8.21*x1 + 6.05*x3 <= 99',
    '10.68*x2 + 10.32*x4 <= 76',
    '1.24*x3 + 1.56*x4 <= 224',
    '7.78*x2 + 1.24*x3 + 1.56*x4 <= 163',
    '5.97*x1 + 7.78*x2 + 1.56*x4 <= 168'
]

# Set the objective function
model.setObjective(8 * x1 + 3 * x2 + 2 * x3 + 3 * x4, gurobi.GRB.MINIMIZE)

# Add constraints
for constraint in constraints:
    model.addConstr(eval(constraint))

# Optimize the model
model.optimize()

# Print the solution
print("Objective: ", model.objval)
print("x1: ", x1.varValue)
print("x2: ", x2.varValue)
print("x3: ", x3.varValue)
print("x4: ", x4.varValue)

# Print symbolic representation
print("Symbolic Representation:")
print("Variables: ", sym_variables)
print("Objective Function: ", objective_function)
print("Constraints: ", constraints)

```
The final answer is: 
```json
{
    'sym_variables': [('x1', 'grams of fat'), ('x2', 'milligrams of magnesium'), 
                      ('x3', 'grams of fiber'), ('x4', 'milligrams of vitamin B3')], 
    'objective_function': '8*x1 + 3*x2 + 2*x3 + 3*x4', 
    'constraints': [
        '3.17*x1 <= 132',
        '8.21*x1 <= 168',
        '5.97*x1 <= 228',
        '5.91*x2 <= 132',
        '10.68*x2 <= 168',
        '7.78*x2 <= 228',
        '4.99*x3 <= 132',
        '6.05*x3 <= 168',
        '1.24*x3 <= 228',
        '7.55*x4 <= 132',
        '10.32*x4 <= 168',
        '1.56*x4 <= 228',
        '4.99*x3 + 7.55*x4 >= 12',
        '3.17*x1 + 4.99*x3 >= 11',
        '5.91*x2 + 7.55*x4 >= 19',
        '3.17*x1 + 7.55*x4 >= 16',
        '3.17*x1 + 5.91*x2 >= 26',
        '3.17*x1 + 5.91*x2 + 7.55*x4 >= 28',
        '3.17*x1 + 5.91*x2 + 4.99*x3 + 7.55*x4 >= 28',
        '8.21*x1 + 10.32*x4 >= 25',
        '8.21*x1 + 6.05*x3 >= 24',
        '10.68*x2 + 10.32*x4 >= 42',
        '10.68*x2 + 6.05*x3 >= 35',
        '6.05*x3 + 10.32*x4 >= 18',
        '10.68*x2 + 6.05*x3 + 10.32*x4 >= 32',
        '8.21*x1 + 6.05*x3 + 10.32*x4 >= 32',
        '8.21*x1 + 10.68*x2 + 10.32*x4 >= 32',
        '10.68*x2 + 6.05*x3 + 10.32*x4 >= 33',
        '8.21*x1 + 6.05*x3 + 10.32*x4 >= 33',
        '8.21*x1 + 10.68*x2 + 10.32*x4 >= 33',
        '10.68*x2 + 6.05*x3 + 10.32*x4 >= 29',
        '8.21*x1 + 6.05*x3 + 10.32*x4 >= 29',
        '8.21*x1 + 10.68*x2 + 10.32*x4 >= 29',
        '8.21*x1 + 10.68*x2 + 6.05*x3 + 10.32*x4 >= 29',
        '5.97*x1 + 1.56*x4 >= 42',
        '7.78*x2 + 1.56*x4 >= 57',
        '5.97*x1 + 7.78*x2 >= 27',
        '5.97*x1 + 1.24*x3 + 1.56*x4 >= 44',
        '5.97*x1 + 7.78*x2 + 1.24*x3 >= 44',
        '5.97*x1 + 1.24*x3 + 1.56*x4 >= 44',
        '5.97*x1 + 7.78*x2 + 1.24*x3 >= 44',
        '5.97*x1 + 7.78*x2 + 1.24*x3 + 1.56*x4 >= 44',
        'x1 - 9*x3 >= 0',
        '8.21*x1 + 6.05*x3 <= 99',
        '10.68*x2 + 10.32*x4 <= 76',
        '1.24*x3 + 1.56*x4 <= 224',
        '7.78*x2 + 1.24*x3 + 1.56*x4 <= 163',
        '5.97*x1 + 7.78*x2 + 1.56*x4 <= 168'
    ]
}
```