## Step 1: Define the symbolic representation of the variables
The variables are defined as follows: 
- $x_1$ = grams of fiber
- $x_2$ = milligrams of vitamin B4
- $x_3$ = grams of fat
- $x_4$ = milligrams of vitamin A
- $x_5$ = milligrams of vitamin B9
- $x_6$ = milligrams of zinc

## Step 2: Define the objective function in symbolic notation
The objective function to minimize is: $5.81x_1 + 5.03x_2 + 5.98x_3 + 5.46x_4 + 7.33x_5 + 4.03x_6$

## Step 3: Define the constraints in symbolic notation
The constraints are:
- $9x_1 \leq 230$
- $5x_2 \leq 230$
- $8x_3 \leq 230$
- $14x_4 \leq 230$
- $10x_5 \leq 230$
- $2x_6 \leq 230$
- $5x_2 + 10x_5 \geq 25$
- $9x_1 + 5x_2 \geq 24$
- $10x_5 + 2x_6 \geq 13$
- $5x_2 + 14x_4 + 2x_6 \geq 35$
- $9x_1 + 14x_4 + 2x_6 \geq 35$
- $9x_1 + 5x_2 + 8x_3 \geq 35$
- $9x_1 + 8x_3 + 2x_6 \geq 35$
- $5x_2 + 14x_4 + 2x_6 \geq 26$
- $9x_1 + 14x_4 + 2x_6 \geq 26$
- $9x_1 + 5x_2 + 8x_3 \geq 26$
- $9x_1 + 8x_3 + 2x_6 \geq 26$
- $5x_2 + 14x_4 + 2x_6 \geq 25$
- $9x_1 + 14x_4 + 2x_6 \geq 25$
- $9x_1 + 5x_2 + 8x_3 \geq 25$
- $9x_1 + 8x_3 + 2x_6 \geq 25$
- $5x_2 + 14x_4 + 2x_6 \geq 30$
- $9x_1 + 14x_4 + 2x_6 \geq 30$
- $9x_1 + 5x_2 + 8x_3 \geq 30$
- $9x_1 + 8x_3 + 2x_6 \geq 30$
- $9x_1 + 5x_2 + 8x_3 + 14x_4 + 10x_5 + 2x_6 \geq 30$
- $-8x_3 + 3x_6 \geq 0$
- $10x_3 - 5x_5 \geq 0$
- $7x_2 - x_6 \geq 0$
- $-7x_1 + 5x_6 \geq 0$
- $-9x_1 + x_2 \geq 0$
- $-6x_2 - 6x_5 + 6x_6 \geq 0$
- $9x_1 + 5x_2 + 2x_6 \leq 167$

## 4: Implement the problem in Gurobi

```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="x1", lb=0)  # grams of fiber
    x2 = model.addVar(name="x2", lb=0)  # milligrams of vitamin B4
    x3 = model.addVar(name="x3", lb=0)  # grams of fat
    x4 = model.addVar(name="x4", lb=0)  # milligrams of vitamin A
    x5 = model.addVar(name="x5", lb=0)  # milligrams of vitamin B9
    x6 = model.addVar(name="x6", lb=0)  # milligrams of zinc

    # Objective function
    model.setObjective(5.81 * x1 + 5.03 * x2 + 5.98 * x3 + 5.46 * x4 + 7.33 * x5 + 4.03 * x6, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(9 * x1 <= 230)
    model.addConstr(5 * x2 <= 230)
    model.addConstr(8 * x3 <= 230)
    model.addConstr(14 * x4 <= 230)
    model.addConstr(10 * x5 <= 230)
    model.addConstr(2 * x6 <= 230)

    model.addConstr(5 * x2 + 10 * x5 >= 25)
    model.addConstr(9 * x1 + 5 * x2 >= 24)
    model.addConstr(10 * x5 + 2 * x6 >= 13)
    model.addConstr(5 * x2 + 14 * x4 + 2 * x6 >= 35)
    model.addConstr(9 * x1 + 14 * x4 + 2 * x6 >= 35)
    model.addConstr(9 * x1 + 5 * x2 + 8 * x3 >= 35)
    model.addConstr(9 * x1 + 8 * x3 + 2 * x6 >= 35)

    model.addConstr(5 * x2 + 14 * x4 + 2 * x6 >= 26)
    model.addConstr(9 * x1 + 14 * x4 + 2 * x6 >= 26)
    model.addConstr(9 * x1 + 5 * x2 + 8 * x3 >= 26)
    model.addConstr(9 * x1 + 8 * x3 + 2 * x6 >= 26)

    model.addConstr(5 * x2 + 14 * x4 + 2 * x6 >= 25)
    model.addConstr(9 * x1 + 14 * x4 + 2 * x6 >= 25)
    model.addConstr(9 * x1 + 5 * x2 + 8 * x3 >= 25)
    model.addConstr(9 * x1 + 8 * x3 + 2 * x6 >= 25)

    model.addConstr(5 * x2 + 14 * x4 + 2 * x6 >= 30)
    model.addConstr(9 * x1 + 14 * x4 + 2 * x6 >= 30)
    model.addConstr(9 * x1 + 5 * x2 + 8 * x3 >= 30)
    model.addConstr(9 * x1 + 8 * x3 + 2 * x6 >= 30)

    model.addConstr(9 * x1 + 5 * x2 + 8 * x3 + 14 * x4 + 10 * x5 + 2 * x6 >= 30)

    model.addConstr(-8 * x3 + 3 * x6 >= 0)
    model.addConstr(10 * x3 - 5 * x5 >= 0)
    model.addConstr(7 * x2 - x6 >= 0)
    model.addConstr(-7 * x1 + 5 * x6 >= 0)
    model.addConstr(-9 * x1 + x2 >= 0)
    model.addConstr(-6 * x2 - 6 * x5 + 6 * x6 >= 0)

    model.addConstr(9 * x1 + 5 * x2 + 2 * x6 <= 167)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objval)
        print("x1: ", x1.varValue)
        print("x2: ", x2.varValue)
        print("x3: ", x3.varValue)
        print("x4: ", x4.varValue)
        print("x5: ", x5.varValue)
        print("x6: ", x6.varValue)
    else:
        print("The model is infeasible")

solve_optimization_problem()
```

## 5: Symbolic Representation

```json
{
    'sym_variables': [
        ('x1', 'grams of fiber'), 
        ('x2', 'milligrams of vitamin B4'), 
        ('x3', 'grams of fat'), 
        ('x4', 'milligrams of vitamin A'), 
        ('x5', 'milligrams of vitamin B9'), 
        ('x6', 'milligrams of zinc')
    ], 
    'objective_function': '5.81*x1 + 5.03*x2 + 5.98*x3 + 5.46*x4 + 7.33*x5 + 4.03*x6', 
    'constraints': [
        '9*x1 <= 230',
        '5*x2 <= 230',
        '8*x3 <= 230',
        '14*x4 <= 230',
        '10*x5 <= 230',
        '2*x6 <= 230',
        '5*x2 + 10*x5 >= 25',
        '9*x1 + 5*x2 >= 24',
        '10*x5 + 2*x6 >= 13',
        '5*x2 + 14*x4 + 2*x6 >= 35',
        '9*x1 + 14*x4 + 2*x6 >= 35',
        '9*x1 + 5*x2 + 8*x3 >= 35',
        '9*x1 + 8*x3 + 2*x6 >= 35',
        '5*x2 + 14*x4 + 2*x6 >= 26',
        '9*x1 + 14*x4 + 2*x6 >= 26',
        '9*x1 + 5*x2 + 8*x3 >= 26',
        '9*x1 + 8*x3 + 2*x6 >= 26',
        '5*x2 + 14*x4 + 2*x6 >= 25',
        '9*x1 + 14*x4 + 2*x6 >= 25',
        '9*x1 + 5*x2 + 8*x3 >= 25',
        '9*x1 + 8*x3 + 2*x6 >= 25',
        '5*x2 + 14*x4 + 2*x6 >= 30',
        '9*x1 + 14*x4 + 2*x6 >= 30',
        '9*x1 + 5*x2 + 8*x3 >= 30',
        '9*x1 + 8*x3 + 2*x6 >= 30',
        '9*x1 + 5*x2 + 8*x3 + 14*x4 + 10*x5 + 2*x6 >= 30',
        '-8*x3 + 3*x6 >= 0',
        '10*x3 - 5*x5 >= 0',
        '7*x2 - x6 >= 0',
        '-7*x1 + 5*x6 >= 0',
        '-9*x1 + x2 >= 0',
        '-6*x2 - 6*x5 + 6*x6 >= 0',
        '9*x1 + 5*x2 + 2*x6 <= 167'
    ]
}
```