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

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_0$ : strips of bacon
- $x_1$ : pickles
- $x_2$ : bowls of pasta

## 3: Define the objective function in symbolic notation
The objective function to minimize is $6.81x_0 + 5.47x_1 + 9.57x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
- $x_0 + 13x_1 + 4x_2 \geq 55$ is not correct based on the problem description, we have:
  - $1x_0 + 4x_2 \geq 55$ (at least 55 milligrams of iron from strips of bacon and bowls of pasta)
  - $1x_0 + 13x_1 \geq 97$ (at least 97 milligrams of iron from strips of bacon and pickles)
  - $1x_0 + 13x_1 + 4x_2 \geq 74$ (at least 74 milligrams of iron from all sources)
  - $10x_0 + 9x_1 + 11x_2 \geq 16$ is not correct, we have:
  - $9x_1 + 11x_2 \geq 16$ (total combined sourness index from pickles and bowls of pasta)
  - $10x_0 + 9x_1 + 11x_2 \geq 16$ (total combined sourness index from all sources)
  - $5x_0 - 3x_2 \geq 0$
  - $7x_0 - 9x_1 \geq 0$

## 5: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [('x0', 'strips of bacon'), ('x1', 'pickles'), ('x2', 'bowls of pasta')],
    'objective_function': '6.81*x0 + 5.47*x1 + 9.57*x2',
    'constraints': [
        '1*x0 + 4*x2 >= 55',
        '1*x0 + 13*x1 >= 97',
        '1*x0 + 13*x1 + 4*x2 >= 74',
        '9*x1 + 11*x2 >= 16',
        '10*x0 + 9*x1 + 11*x2 >= 16',
        '5*x0 - 3*x2 >= 0',
        '7*x0 - 9*x1 >= 0'
    ]
}
```

## 6: Write the Gurobi code to solve the optimization problem
```python
import gurobi

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

    # Define the variables
    x0 = model.addVar(name="strips_of_bacon", lb=0)  # strips of bacon
    x1 = model.addVar(name="pickles", lb=0)  # pickles
    x2 = model.addVar(name="bowls_of_pasta", lb=0)  # bowls of pasta

    # Define the objective function
    model.setObjective(6.81 * x0 + 5.47 * x1 + 9.57 * x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(1 * x0 + 4 * x2 >= 55)  # at least 55 milligrams of iron from strips of bacon and bowls of pasta
    model.addConstr(1 * x0 + 13 * x1 >= 97)  # at least 97 milligrams of iron from strips of bacon and pickles
    model.addConstr(1 * x0 + 13 * x1 + 4 * x2 >= 74)  # at least 74 milligrams of iron from all sources
    model.addConstr(9 * x1 + 11 * x2 >= 16)  # total combined sourness index from pickles and bowls of pasta
    model.addConstr(10 * x0 + 9 * x1 + 11 * x2 >= 16)  # total combined sourness index from all sources
    model.addConstr(5 * x0 - 3 * x2 >= 0)  
    model.addConstr(7 * x0 - 9 * x1 >= 0)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objVal)
        print("Strips of bacon: ", x0.varValue)
        print("Pickles: ", x1.varValue)
        print("Bowls of pasta: ", x2.varValue)
    else:
        print("The model is infeasible")

solve_optimization_problem()
```