## Problem Description and Symbolic Representation

The problem requires maximizing the objective function: $7 \times \text{cornichons} + 1 \times (\text{strips of bacon} + 1) \times \text{slices of pizza}$.

Let's denote the variables as follows:
- $x_1$ for cornichons
- $x_2$ for strips of bacon
- $x_3$ for slices of pizza

The objective function in symbolic notation is: $7x_1 + (x_2 + 1)x_3$.

The constraints are:
- $7x_1 + 6x_2 \leq 22$ (no more than 22 grams of fiber from cornichons and strips of bacon)
- $7x_1 + x_3 \leq 22$ (no more than 22 grams of fiber from cornichons and slices of pizza)
- $7x_1 + 6x_2 + x_3 \leq 23$ (no more than 23 grams of fiber from all sources)
- $x_1$ can be non-integer
- $x_2$ can be non-integer
- $x_3$ must be integer

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [
        ('x1', 'cornichons'), 
        ('x2', 'strips of bacon'), 
        ('x3', 'slices of pizza')
    ], 
    'objective_function': '7*x1 + (x2 + 1)*x3', 
    'constraints': [
        '7*x1 + 6*x2 <= 22', 
        '7*x1 + x3 <= 22', 
        '7*x1 + 6*x2 + x3 <= 23'
    ]
}
```

## Gurobi Code

```python
import gurobi

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

    # Define variables
    x1 = model.addVar(name="cornichons", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
    x2 = model.addVar(name="strips_of_bacon", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
    x3 = model.addVar(name="slices_of_pizza", lb=0, ub=gurobi.GRB.INFINITY, vtype=gurobi.GRB.INTEGER)

    # Objective function
    model.setObjective(7 * x1 + (x2 + 1) * x3, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(7 * x1 + 6 * x2 <= 22, name="fiber_from_cornichons_and_bacon")
    model.addConstr(7 * x1 + x3 <= 22, name="fiber_from_cornichons_and_pizza")
    model.addConstr(7 * x1 + 6 * x2 + x3 <= 23, name="fiber_from_all_sources")

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Cornichons: {x1.varValue}")
        print(f"Strips of bacon: {x2.varValue}")
        print(f"Slices of pizza: {x3.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```