## Step 1: Define the symbolic representation of the variables
The variables are 'apples', 'green beans', and 'cornichons', which can be represented symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the natural language description into a symbolic representation
The symbolic representation of the variables is:
- $x_0$ : apples
- $x_1$ : green beans
- $x_2$ : cornichons

## Step 3: Define the objective function in symbolic notation
The objective function to minimize is $2.93x_0 + 2.28x_1 + 9.24x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
1. $8x_0 + 6x_1 + 11x_2 \geq 70$ (at least 70 grams of carbohydrates from apples, green beans, and cornichons)
2. $8x_0 + 11x_2 \geq 70$ (at least 70 grams of carbohydrates from apples and cornichons)
3. $17x_1 + 12x_2 \geq 31$ (at least 31 milligrams of iron from green beans and cornichons)
4. $23x_0 + 12x_2 \geq 33$ (at least 33 milligrams of iron from apples and cornichons)
5. $23x_0 + 17x_1 + 12x_2 \geq 33$ (at least 33 milligrams of iron from apples, green beans, and cornichons)
6. $-2x_0 + 2x_1 \geq 0$ (minus two times the number of apples, plus two times the number of green beans must be greater than or equal to zero)
7. $9x_0 - 5x_2 \geq 0$ (9 times the number of apples, plus -5 times the number of cornichons has to be no less than zero)
8. $6x_1 + 11x_2 \leq 155$ (no more than 155 grams of carbohydrates from green beans and cornichons)
9. $8x_0 + 6x_1 + 11x_2 \leq 182$ (no more than 182 grams of carbohydrates from apples, green beans, and cornichons)
10. $17x_1 + 12x_2 \leq 108$ (no more than 108 milligrams of iron from green beans and cornichons)
11. $23x_0 + 17x_1 \leq 64$ (no more than 64 milligrams of iron from apples and green beans)
12. $23x_0 + 17x_1 + 12x_2 \leq 87$ (no more than 87 milligrams of iron from apples, green beans, and cornichons)
13. $x_0$ is continuous
14. $x_1$ is integer
15. $x_2$ is continuous

## 5: Define the resources/attributes constraints
Given:
- $r_0$ (carbohydrates): $8x_0 + 6x_1 + 11x_2 \leq 247$
- $r_1$ (iron): $23x_0 + 17x_1 + 12x_2 \leq 165$

These need to be considered in the context of the problem but are implicitly included in the constraints listed.

## 6: Create the symbolic representation dictionary
```json
{
    'sym_variables': [('x0', 'apples'), ('x1', 'green beans'), ('x2', 'cornichons')],
    'objective_function': '2.93*x0 + 2.28*x1 + 9.24*x2',
    'constraints': [
        '8*x0 + 6*x1 + 11*x2 >= 70',
        '8*x0 + 11*x2 >= 70',
        '17*x1 + 12*x2 >= 31',
        '23*x0 + 12*x2 >= 33',
        '23*x0 + 17*x1 + 12*x2 >= 33',
        '-2*x0 + 2*x1 >= 0',
        '9*x0 - 5*x2 >= 0',
        '6*x1 + 11*x2 <= 155',
        '8*x0 + 6*x1 + 11*x2 <= 182',
        '17*x1 + 12*x2 <= 108',
        '23*x0 + 17*x1 <= 64',
        '23*x0 + 17*x1 + 12*x2 <= 87',
        '8*x0 + 6*x1 + 11*x2 <= 247',
        '23*x0 + 17*x1 + 12*x2 <= 165'
    ]
}
```

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

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

    # Define variables
    x0 = model.addVar(name="apples", lb=0, ub=None, vtype=gurobi.GRB.CONTINUOUS)
    x1 = model.addVar(name="green_beans", lb=0, vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="cornichons", lb=0, ub=None, vtype=gurobi.GRB.CONTINUOUS)

    # Define objective function
    model.setObjective(2.93*x0 + 2.28*x1 + 9.24*x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(8*x0 + 6*x1 + 11*x2 >= 70)
    model.addConstr(8*x0 + 11*x2 >= 70)
    model.addConstr(17*x1 + 12*x2 >= 31)
    model.addConstr(23*x0 + 12*x2 >= 33)
    model.addConstr(23*x0 + 17*x1 + 12*x2 >= 33)
    model.addConstr(-2*x0 + 2*x1 >= 0)
    model.addConstr(9*x0 - 5*x2 >= 0)
    model.addConstr(6*x1 + 11*x2 <= 155)
    model.addConstr(8*x0 + 6*x1 + 11*x2 <= 182)
    model.addConstr(17*x1 + 12*x2 <= 108)
    model.addConstr(23*x0 + 17*x1 <= 64)
    model.addConstr(23*x0 + 17*x1 + 12*x2 <= 87)
    model.addConstr(8*x0 + 6*x1 + 11*x2 <= 247)
    model.addConstr(23*x0 + 17*x1 + 12*x2 <= 165)

    # Solve the model
    model.optimize()

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

solve_optimization_problem()
```