## Step 1: Define the symbolic representation of the problem
The problem involves minimizing an objective function subject to several constraints. The variables are 'oranges', 'bowls of pasta', and 'cornichons', which we can represent symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to minimize is $5.56x_0 + 8.24x_1 + 7.5x_2$.

## 3: List the constraints in symbolic notation
The constraints given are:
1. $x_0 + 4x_1 + 2x_2 \geq 32$ (protein from oranges, bowls of pasta, and cornichons)
2. $x_0 + 2x_2 \geq 26$ (protein from oranges and cornichons)
3. $x_0 + 4x_1 + 2x_2 \geq 26$ (same as constraint 1, redundant)
4. $2x_1 + 7x_2 \geq 8$ (carbohydrates from bowls of pasta and cornichons)
5. $5x_0 + 2x_1 \geq 6$ (carbohydrates from oranges and bowls of pasta)
6. $5x_0 + 2x_1 + 7x_2 \geq 6$ (carbohydrates from all sources)
7. $x_1 + 8x_2 \geq 40$ (fat from bowls of pasta and cornichons)
8. $7x_0 + x_1 \geq 35$ (fat from oranges and bowls of pasta)
9. $7x_0 + x_1 + 8x_2 \geq 35$ (fat from all sources)
10. $-x_0 + 7x_1 \geq 0$ (relationship between oranges and bowls of pasta)
11. $5x_0 + 2x_1 + 7x_2 \leq 42$ (carbohydrates upper bound)

## 4: Define the symbolic variables and constraints for output
The symbolic variables are:
- $x_0$ for 'oranges'
- $x_1$ for 'bowls of pasta'
- $x_2$ for 'cornichons'

## 5: Write down the problem in a structured format
```json
{
    'sym_variables': [('x0', 'oranges'), ('x1', 'bowls of pasta'), ('x2', 'cornichons')],
    'objective_function': '5.56*x0 + 8.24*x1 + 7.5*x2',
    'constraints': [
        'x0 + 4*x1 + 2*x2 >= 32',
        'x0 + 2*x2 >= 26',
        '2*x1 + 7*x2 >= 8',
        '5*x0 + 2*x1 >= 6',
        '5*x0 + 2*x1 + 7*x2 >= 6',
        'x1 + 8*x2 >= 40',
        '7*x0 + x1 >= 35',
        '7*x0 + x1 + 8*x2 >= 35',
        '-x0 + 7*x1 >= 0',
        '5*x0 + 2*x1 + 7*x2 <= 42'
    ]
}
```

## 6: Implement the problem using Gurobi
```python
import gurobi

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

# Define the variables
oranges = m.addVar(name="oranges", lb=0)
bowls_of_pasta = m.addVar(name="bowls_of_pasta", lb=0)
cornichons = m.addVar(name="cornichons", lb=0)

# Define the objective function
m.setObjective(5.56 * oranges + 8.24 * bowls_of_pasta + 7.5 * cornichons, gurobi.GRB.MINIMIZE)

# Add constraints
m.addConstr(oranges + 4 * bowls_of_pasta + 2 * cornichons >= 32, name="protein_total")
m.addConstr(oranges + 2 * cornichons >= 26, name="protein_oranges_cornichons")
m.addConstr(2 * bowls_of_pasta + 7 * cornichons >= 8, name="carbohydrates_pasta_cornichons")
m.addConstr(5 * oranges + 2 * bowls_of_pasta >= 6, name="carbohydrates_oranges_pasta")
m.addConstr(5 * oranges + 2 * bowls_of_pasta + 7 * cornichons >= 6, name="carbohydrates_all")
m.addConstr(bowls_of_pasta + 8 * cornichons >= 40, name="fat_pasta_cornichons")
m.addConstr(7 * oranges + bowls_of_pasta >= 35, name="fat_oranges_pasta")
m.addConstr(7 * oranges + bowls_of_pasta + 8 * cornichons >= 35, name="fat_all")
m.addConstr(-oranges + 7 * bowls_of_pasta >= 0, name="oranges_pasta_relationship")
m.addConstr(5 * oranges + 2 * bowls_of_pasta + 7 * cornichons <= 42, name="carbohydrates_upper_bound")

# Optimize the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("Oranges: ", oranges.varValue)
    print("Bowls of pasta: ", bowls_of_pasta.varValue)
    print("Cornichons: ", cornichons.varValue)
else:
    print("The model is infeasible")
```