## Step 1: Define the symbolic representation of the problem
The problem involves variables 'oranges', 'milkshakes', and 'pickles', which we will denote as $x_0$, $x_1$, and $x_2$ respectively. The objective function to minimize is $2.69x_0 + 1.69x_1 + 1.85x_2$. The constraints are:
- $2x_0 + 9x_1 + 10x_2 \leq 127$ (total fat)
- $11x_0 + 16x_1 + 13x_2 \leq 129$ (total carbohydrates)
- $2x_0 + 10x_2 \geq 33$ (fat from oranges and pickles)
- $2x_0 + 9x_1 \geq 36$ (fat from oranges and milkshakes)
- $2x_0 + 9x_1 + 10x_2 \geq 36$ (total fat from all)
- $11x_0 + 13x_2 \geq 31$ (carbohydrates from oranges and pickles)
- $11x_0 + 16x_1 \geq 25$ (carbohydrates from oranges and milkshakes)
- $11x_0 + 16x_1 + 13x_2 \geq 25$ (total carbohydrates from all)
- $-8x_0 + 4x_1 \geq 0$
- $2x_1 - 9x_2 \geq 0$
- $9x_1 + 10x_2 \leq 91$ (fat from milkshakes and pickles)
- $16x_1 + 13x_2 \leq 97$ (carbohydrates from milkshakes and pickles)

## Step 2: Convert the problem into a Gurobi-compatible format
We need to define the variables, the objective function, and the constraints in a format that Gurobi can understand.

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

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

    # Define the variables
    oranges = model.addVar(name="oranges", lb=0)
    milkshakes = model.addVar(name="milkshakes", lb=0)
    pickles = model.addVar(name="pickles", lb=0)

    # Define the objective function
    model.setObjective(2.69 * oranges + 1.69 * milkshakes + 1.85 * pickles, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(2 * oranges + 9 * milkshakes + 10 * pickles <= 127, name="total_fat")
    model.addConstr(11 * oranges + 16 * milkshakes + 13 * pickles <= 129, name="total_carbohydrates")
    model.addConstr(2 * oranges + 10 * pickles >= 33, name="fat_oranges_pickles")
    model.addConstr(2 * oranges + 9 * milkshakes >= 36, name="fat_oranges_milkshakes")
    model.addConstr(2 * oranges + 9 * milkshakes + 10 * pickles >= 36, name="total_fat_all")
    model.addConstr(11 * oranges + 13 * pickles >= 31, name="carbohydrates_oranges_pickles")
    model.addConstr(11 * oranges + 16 * milkshakes >= 25, name="carbohydrates_oranges_milkshakes")
    model.addConstr(11 * oranges + 16 * milkshakes + 13 * pickles >= 25, name="total_carbohydrates_all")
    model.addConstr(-8 * oranges + 4 * milkshakes >= 0, name="oranges_milkshakes_relation")
    model.addConstr(2 * milkshakes - 9 * pickles >= 0, name="milkshakes_pickles_relation")
    model.addConstr(9 * milkshakes + 10 * pickles <= 91, name="fat_milkshakes_pickles")
    model.addConstr(16 * milkshakes + 13 * pickles <= 97, name="carbohydrates_milkshakes_pickles")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Oranges: {oranges.varValue}")
        print(f"Milkshakes: {milkshakes.varValue}")
        print(f"Pickles: {pickles.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```

## 4: Symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'oranges'), 
        ('x1', 'milkshakes'), 
        ('x2', 'pickles')
    ], 
    'objective_function': '2.69*x0 + 1.69*x1 + 1.85*x2', 
    'constraints': [
        '2*x0 + 9*x1 + 10*x2 <= 127', 
        '11*x0 + 16*x1 + 13*x2 <= 129', 
        '2*x0 + 10*x2 >= 33', 
        '2*x0 + 9*x1 >= 36', 
        '2*x0 + 9*x1 + 10*x2 >= 36', 
        '11*x0 + 13*x2 >= 31', 
        '11*x0 + 16*x1 >= 25', 
        '11*x0 + 16*x1 + 13*x2 >= 25', 
        '-8*x0 + 4*x1 >= 0', 
        '2*x1 - 9*x2 >= 0', 
        '9*x1 + 10*x2 <= 91', 
        '16*x1 + 13*x2 <= 97'
    ]
}
```