## Step 1: Define the symbolic representation of the variables
The variables are 'bowls of pasta', 'ham sandwiches', 'milkshakes', and 'strips of bacon', which can be represented symbolically as $x_0, x_1, x_2, x_3$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables and their corresponding natural language objects are:
- $x_0$ : 'bowls of pasta'
- $x_1$ : 'ham sandwiches'
- $x_2$ : 'milkshakes'
- $x_3$ : 'strips of bacon'

## Step 3: Define the objective function in symbolic notation
The objective function to maximize is $9x_0 + 7x_1 + 7x_2 + 6x_3$.

## Step 4: List the constraints in symbolic notation
The constraints are:
- $1.47x_1 + 7.28x_2 \geq 28$ (at least 28 grams of fiber from ham sandwiches and milkshakes)
- $5.6x_0 + 1.47x_1 \geq 9$ (at least 9 grams of fiber from bowls of pasta and ham sandwiches)
- $1.47x_1 + 4.38x_3 \geq 28$ (at least 28 grams of fiber from ham sandwiches and strips of bacon)
- $5.6x_0 + 7.28x_2 \geq 17$ (at least 17 grams of fiber from bowls of pasta and milkshakes)
- $6.23x_2 + 1.15x_3 \geq 9$ (total sourness index from milkshakes and strips of bacon)
- $4.32x_0 + 6.23x_2 \geq 19$ (total sourness index from bowls of pasta and milkshakes)
- $4.32x_0 + 1.15x_3 \geq 8$ (total sourness index from bowls of pasta and strips of bacon)
- $3.11x_1 + 6.23x_2 \geq 20$ (total sourness index from ham sandwiches and milkshakes)
- $4.32x_0 + 3.11x_1 + 6.23x_2 \geq 15$ (total sourness index from bowls of pasta, ham sandwiches, and milkshakes)
- $1.47x_1 + 7.28x_2 \leq 66$ (at most 66 grams of fiber from ham sandwiches and milkshakes)
- $5.6x_0 + 1.47x_1 \leq 51$ (at most 51 grams of fiber from bowls of pasta and ham sandwiches)
- $5.6x_0 + 4.38x_3 \leq 85$ (at most 85 grams of fiber from bowls of pasta and strips of bacon)
- $5.6x_0 + 1.47x_1 + 7.28x_2 + 4.38x_3 \leq 85$ (at most 85 grams of fiber from all sources)
- $4.32x_0 + 3.11x_1 \leq 71$ (total sourness index from bowls of pasta and ham sandwiches)
- $6.23x_2 + 1.15x_3 \leq 37$ (total sourness index from milkshakes and strips of bacon)
- $3.11x_1 + 1.15x_3 \leq 30$ (total sourness index from ham sandwiches and strips of bacon)
- $4.32x_0 + 6.23x_2 \leq 65$ (total sourness index from bowls of pasta and milkshakes)
- $4.32x_0 + 3.11x_1 + 6.23x_2 + 1.15x_3 \leq 65$ (total sourness index from all sources)

## 5: Define the variable bounds
- $x_0$ is an integer (whole number of bowls of pasta)
- $x_1, x_2, x_3$ are continuous (fractional amounts allowed)

## 6: Write the Gurobi code
```python
import gurobi

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

# Define the variables
x0 = m.addVar(name='bowls_of_pasta', vtype='I')  # integer
x1 = m.addVar(name='ham_sandwiches')  # continuous
x2 = m.addVar(name='milkshakes')  # continuous
x3 = m.addVar(name='strips_of_bacon')  # continuous

# Define the objective function
m.setObjective(9 * x0 + 7 * x1 + 7 * x2 + 6 * x3, gurobi.GRB.MAXIMIZE)

# Add constraints
m.addConstr(1.47 * x1 + 7.28 * x2 >= 28)
m.addConstr(5.6 * x0 + 1.47 * x1 >= 9)
m.addConstr(1.47 * x1 + 4.38 * x3 >= 28)
m.addConstr(5.6 * x0 + 7.28 * x2 >= 17)
m.addConstr(6.23 * x2 + 1.15 * x3 >= 9)
m.addConstr(4.32 * x0 + 6.23 * x2 >= 19)
m.addConstr(4.32 * x0 + 1.15 * x3 >= 8)
m.addConstr(3.11 * x1 + 6.23 * x2 >= 20)
m.addConstr(4.32 * x0 + 3.11 * x1 + 6.23 * x2 >= 15)
m.addConstr(1.47 * x1 + 7.28 * x2 <= 66)
m.addConstr(5.6 * x0 + 1.47 * x1 <= 51)
m.addConstr(5.6 * x0 + 4.38 * x3 <= 85)
m.addConstr(5.6 * x0 + 1.47 * x1 + 7.28 * x2 + 4.38 * x3 <= 85)
m.addConstr(4.32 * x0 + 3.11 * x1 <= 71)
m.addConstr(6.23 * x2 + 1.15 * x3 <= 37)
m.addConstr(3.11 * x1 + 1.15 * x3 <= 30)
m.addConstr(4.32 * x0 + 6.23 * x2 <= 65)
m.addConstr(4.32 * x0 + 3.11 * x1 + 6.23 * x2 + 1.15 * x3 <= 65)

# Optimize the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.OPTIMAL:
    print('Objective: ', m.objVal)
    print('bowls of pasta: ', x0.varValue)
    print('ham sandwiches: ', x1.varValue)
    print('milkshakes: ', x2.varValue)
    print('strips of bacon: ', x3.varValue)
else:
    print('No optimal solution found')
```

## 7: Symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'bowls of pasta'),
        ('x1', 'ham sandwiches'),
        ('x2', 'milkshakes'),
        ('x3', 'strips of bacon')
    ],
    'objective_function': '9*x0 + 7*x1 + 7*x2 + 6*x3',
    'constraints': [
        '1.47*x1 + 7.28*x2 >= 28',
        '5.6*x0 + 1.47*x1 >= 9',
        '1.47*x1 + 4.38*x3 >= 28',
        '5.6*x0 + 7.28*x2 >= 17',
        '6.23*x2 + 1.15*x3 >= 9',
        '4.32*x0 + 6.23*x2 >= 19',
        '4.32*x0 + 1.15*x3 >= 8',
        '3.11*x1 + 6.23*x2 >= 20',
        '4.32*x0 + 3.11*x1 + 6.23*x2 >= 15',
        '1.47*x1 + 7.28*x2 <= 66',
        '5.6*x0 + 1.47*x1 <= 51',
        '5.6*x0 + 4.38*x3 <= 85',
        '5.6*x0 + 1.47*x1 + 7.28*x2 + 4.38*x3 <= 85',
        '4.32*x0 + 3.11*x1 <= 71',
        '6.23*x2 + 1.15*x3 <= 37',
        '3.11*x1 + 1.15*x3 <= 30',
        '4.32*x0 + 6.23*x2 <= 65',
        '4.32*x0 + 3.11*x1 + 6.23*x2 + 1.15*x3 <= 65'
    ]
}
```