## Step 1: Define the symbolic representation of the variables
The variables are defined as follows: 
- $x_1$ represents 'milligrams of vitamin B9'
- $x_2$ represents 'milligrams of vitamin B4'
- $x_3$ represents 'grams of fiber'
- $x_4$ represents 'milligrams of vitamin K'

## Step 2: Convert the natural language description into a symbolic representation
The objective function to maximize is $7x_1 + 8x_2 + 7x_3 + 5x_4$.

## 3: List the constraints in symbolic notation
The constraints are:
- $11x_1 \leq 79$ 
- $9x_2 \leq 79$ 
- $11x_3 \leq 79$ 
- $3x_4 \leq 79$ 
- $-8x_2 + x_3 \geq 0$ 
- $9x_2 + 3x_4 \leq 64$ 
- $11x_1 + 9x_2 \leq 75$ 
- $11x_1 + 11x_3 \leq 49$ 
- $11x_3 + 3x_4 \leq 45$ 
- $11x_1 + 3x_4 \leq 30$ 
- $11x_1 + 9x_2 + 11x_3 + 3x_4 \leq 30$

## 4: Provide the symbolic representation in JSON format
```json
{
    'sym_variables': [
        ('x1', 'milligrams of vitamin B9'), 
        ('x2', 'milligrams of vitamin B4'), 
        ('x3', 'grams of fiber'), 
        ('x4', 'milligrams of vitamin K')
    ], 
    'objective_function': '7*x1 + 8*x2 + 7*x3 + 5*x4', 
    'constraints': [
        '11*x1 <= 79', 
        '9*x2 <= 79', 
        '11*x3 <= 79', 
        '3*x4 <= 79', 
        '-8*x2 + x3 >= 0', 
        '9*x2 + 3*x4 <= 64', 
        '11*x1 + 9*x2 <= 75', 
        '11*x1 + 11*x3 <= 49', 
        '11*x3 + 3*x4 <= 45', 
        '11*x1 + 3*x4 <= 30', 
        '11*x1 + 9*x2 + 11*x3 + 3*x4 <= 30'
    ]
}
```

## Step 5: Write the Gurobi code to solve the optimization problem
```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)  # milligrams of vitamin B9
    x2 = model.addVar(name='x2', lb=0)  # milligrams of vitamin B4
    x3 = model.addVar(name='x3', lb=0)  # grams of fiber
    x4 = model.addVar(name='x4', lb=0)  # milligrams of vitamin K

    # Define the objective function
    model.setObjective(7 * x1 + 8 * x2 + 7 * x3 + 5 * x4, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(11 * x1 <= 79)
    model.addConstr(9 * x2 <= 79)
    model.addConstr(11 * x3 <= 79)
    model.addConstr(3 * x4 <= 79)
    model.addConstr(-8 * x2 + x3 >= 0)
    model.addConstr(9 * x2 + 3 * x4 <= 64)
    model.addConstr(11 * x1 + 9 * x2 <= 75)
    model.addConstr(11 * x1 + 11 * x3 <= 49)
    model.addConstr(11 * x3 + 3 * x4 <= 45)
    model.addConstr(11 * x1 + 3 * x4 <= 30)
    model.addConstr(11 * x1 + 9 * x2 + 11 * x3 + 3 * x4 <= 30)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print('Optimal solution found.')
        print(f'milligrams of vitamin B9: {x1.varValue}')
        print(f'milligrams of vitamin B4: {x2.varValue}')
        print(f'grams of fiber: {x3.varValue}')
        print(f'milligrams of vitamin K: {x4.varValue}')
        print(f'Objective function value: {model.objVal}')
    else:
        print('No optimal solution found.')

solve_optimization_problem()
```