## Step 1: Define the symbolic variables and their corresponding natural language objects.
The symbolic variables are 'x0', 'x1', 'x2', 'x3' which correspond to 'strips of bacon', 'bowls of pasta', 'oranges', 'corn cobs' respectively.

## Step 2: Formulate the objective function using the symbolic variables.
The objective function to minimize is $4.79x_0 + 6.54x_1 + 7.91x_2 + 1.18x_3$.

## Step 3: List all the constraints in symbolic notation.
Constraints:
1. $25x_0 + 25x_1 + 5x_2 + 13x_3 \leq 314$ (iron upper bound)
2. $22x_0 + 15x_1 + 23x_2 + 18x_3 \leq 281$ (fat upper bound)
3. $25x_0 + 25x_1 \geq 42$ (iron from bacon and pasta)
4. $5x_2 + 13x_3 \geq 54$ (iron from oranges and corn cobs)
5. $25x_1 + 13x_3 \geq 46$ (iron from pasta and corn cobs)
6. $25x_0 + 13x_3 \geq 72$ (iron from bacon and corn cobs)
7. $25x_0 + 25x_1 + 5x_2 + 13x_3 \geq 72$ (total iron)
8. $22x_0 + 15x_1 \geq 45$ (fat from bacon and pasta)
9. $22x_0 + 23x_2 \geq 26$ (fat from bacon and oranges)
10. $15x_1 + 18x_3 \geq 36$ (fat from pasta and corn cobs)
11. $15x_1 + 23x_2 \geq 34$ (fat from pasta and oranges)
12. $22x_0 + 18x_3 \geq 57$ (fat from bacon and corn cobs)
13. $22x_0 + 23x_2 + 18x_3 \geq 48$ (fat from bacon, oranges, and corn cobs)
14. $22x_0 + 15x_1 + 23x_2 \geq 48$ (fat from bacon, pasta, and oranges)
15. $15x_1 + 23x_2 + 18x_3 \geq 48$ (fat from pasta, oranges, and corn cobs)
16. $22x_0 + 23x_2 + 18x_3 \geq 66$ (fat from bacon, oranges, and corn cobs)
17. $22x_0 + 15x_1 + 23x_2 \geq 66$ (fat from bacon, pasta, and oranges)
18. $15x_1 + 23x_2 + 18x_3 \geq 66$ (fat from pasta, oranges, and corn cobs)
19. $22x_0 + 23x_2 + 18x_3 \geq 70$ (fat from bacon, oranges, and corn cobs)
20. $22x_0 + 15x_1 + 23x_2 \geq 70$ (fat from bacon, pasta, and oranges)
21. $15x_1 + 23x_2 + 18x_3 \geq 70$ (fat from pasta, oranges, and corn cobs)
22. $22x_0 + 15x_1 + 23x_2 + 18x_3 \geq 70$ (total fat)
23. $9x_1 - 4x_3 \geq 0$
24. $4x_0 - 4x_3 \geq 0$
25. $25x_0 + 25x_1 + 5x_2 \leq 302$ (iron from bacon, pasta, and oranges)
26. $25x_0 + 25x_1 + 13x_3 \leq 306$ (iron from bacon, pasta, and corn cobs)
27. $22x_0 + 23x_2 \leq 124$ (fat from bacon and oranges)
28. $22x_0 + 15x_1 \leq 241$ (fat from bacon and pasta)
29. $23x_2 + 18x_3 \leq 151$ (fat from oranges and corn cobs)
30. $15x_1 + 18x_3 \leq 212$ (fat from pasta and corn cobs)
31. $15x_1 + 23x_2 \leq 279$ (fat from pasta and oranges)
32. $15x_1 + 23x_2 + 18x_3 \leq 172$ (fat from pasta, oranges, and corn cobs)
33. $22x_0 + 15x_1 + 18x_3 \leq 139$ (fat from bacon, pasta, and corn cobs)

## Step 4: Provide the symbolic representation of the problem.
```json
{
'sym_variables': [('x0', 'strips of bacon'), ('x1', 'bowls of pasta'), ('x2', 'oranges'), ('x3', 'corn cobs')],
'objective_function': '4.79*x0 + 6.54*x1 + 7.91*x2 + 1.18*x3',
'constraints': [
'25*x0 + 25*x1 + 5*x2 + 13*x3 <= 314',
'22*x0 + 15*x1 + 23*x2 + 18*x3 <= 281',
'25*x0 + 25*x1 >= 42',
'5*x2 + 13*x3 >= 54',
'25*x1 + 13*x3 >= 46',
'25*x0 + 13*x3 >= 72',
'25*x0 + 25*x1 + 5*x2 + 13*x3 >= 72',
'22*x0 + 15*x1 >= 45',
'22*x0 + 23*x2 >= 26',
'15*x1 + 18*x3 >= 36',
'15*x1 + 23*x2 >= 34',
'22*x0 + 18*x3 >= 57',
'22*x0 + 23*x2 + 18*x3 >= 48',
'22*x0 + 15*x1 + 23*x2 >= 48',
'15*x1 + 23*x2 + 18*x3 >= 48',
'22*x0 + 23*x2 + 18*x3 >= 66',
'22*x0 + 15*x1 + 23*x2 >= 66',
'15*x1 + 23*x2 + 18*x3 >= 66',
'22*x0 + 23*x2 + 18*x3 >= 70',
'22*x0 + 15*x1 + 23*x2 >= 70',
'15*x1 + 23*x2 + 18*x3 >= 70',
'22*x0 + 15*x1 + 23*x2 + 18*x3 >= 70',
'9*x1 - 4*x3 >= 0',
'4*x0 - 4*x3 >= 0',
'25*x0 + 25*x1 + 5*x2 <= 302',
'25*x0 + 25*x1 + 13*x3 <= 306',
'22*x0 + 23*x2 <= 124',
'22*x0 + 15*x1 <= 241',
'23*x2 + 18*x3 <= 151',
'15*x1 + 18*x3 <= 212',
'15*x1 + 23*x2 <= 279',
'15*x1 + 23*x2 + 18*x3 <= 172',
'22*x0 + 15*x1 + 18*x3 <= 139'
]
}
```

## Step 5: Write the Gurobi code to solve the optimization problem.
```python
import gurobi

def solve_optimization_problem():
    model = gurobi.Model()

    # Define variables
    x0 = model.addVar(name="x0", lb=0)  # strips of bacon
    x1 = model.addVar(name="x1", lb=0)  # bowls of pasta
    x2 = model.addVar(name="x2", lb=0)  # oranges
    x3 = model.addVar(name="x3", lb=0)  # corn cobs

    # Objective function
    model.setObjective(4.79 * x0 + 6.54 * x1 + 7.91 * x2 + 1.18 * x3, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(25 * x0 + 25 * x1 + 5 * x2 + 13 * x3 <= 314)
    model.addConstr(22 * x0 + 15 * x1 + 23 * x2 + 18 * x3 <= 281)
    model.addConstr(25 * x0 + 25 * x1 >= 42)
    model.addConstr(5 * x2 + 13 * x3 >= 54)
    model.addConstr(25 * x1 + 13 * x3 >= 46)
    model.addConstr(25 * x0 + 13 * x3 >= 72)
    model.addConstr(25 * x0 + 25 * x1 + 5 * x2 + 13 * x3 >= 72)
    model.addConstr(22 * x0 + 15 * x1 >= 45)
    model.addConstr(22 * x0 + 23 * x2 >= 26)
    model.addConstr(15 * x1 + 18 * x3 >= 36)
    model.addConstr(15 * x1 + 23 * x2 >= 34)
    model.addConstr(22 * x0 + 18 * x3 >= 57)
    model.addConstr(22 * x0 + 23 * x2 + 18 * x3 >= 48)
    model.addConstr(22 * x0 + 15 * x1 + 23 * x2 >= 48)
    model.addConstr(15 * x1 + 23 * x2 + 18 * x3 >= 48)
    model.addConstr(22 * x0 + 23 * x2 + 18 * x3 >= 66)
    model.addConstr(22 * x0 + 15 * x1 + 23 * x2 >= 66)
    model.addConstr(15 * x1 + 23 * x2 + 18 * x3 >= 66)
    model.addConstr(22 * x0 + 23 * x2 + 18 * x3 >= 70)
    model.addConstr(22 * x0 + 15 * x1 + 23 * x2 >= 70)
    model.addConstr(15 * x1 + 23 * x2 + 18 * x3 >= 70)
    model.addConstr(22 * x0 + 15 * x1 + 23 * x2 + 18 * x3 >= 70)
    model.addConstr(9 * x1 - 4 * x3 >= 0)
    model.addConstr(4 * x0 - 4 * x3 >= 0)
    model.addConstr(25 * x0 + 25 * x1 + 5 * x2 <= 302)
    model.addConstr(25 * x0 + 25 * x1 + 13 * x3 <= 306)
    model.addConstr(22 * x0 + 23 * x2 <= 124)
    model.addConstr(22 * x0 + 15 * x1 <= 241)
    model.addConstr(23 * x2 + 18 * x3 <= 151)
    model.addConstr(15 * x1 + 18 * x3 <= 212)
    model.addConstr(15 * x1 + 23 * x2 <= 279)
    model.addConstr(15 * x1 + 23 * x2 + 18 * x3 <= 172)
    model.addConstr(22 * x0 + 15 * x1 + 18 * x3 <= 139)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objVal)
        print("x0: ", x0.varValue)
        print("x1: ", x1.varValue)
        print("x2: ", x2.varValue)
        print("x3: ", x3.varValue)
    else:
        print("The model is infeasible")

solve_optimization_problem()
```