## Problem Description and Symbolic Representation

The problem involves a fruit store that wants to liquidate its stock of lemons, mangos, and pears by preparing two types of packages: mango-lovers packages and regular packages. The goal is to maximize the net profit.

### Symbolic Representation:

Let's denote:
- $x_1$ as the number of mango-lovers packages
- $x_2$ as the number of regular packages

The profit from each mango-lovers package is 5 euros, and from each regular package is 6.5 euros.

The constraints based on the availability of stock are:
- Lemons: $4x_1 + 3x_2 \leq 30$
- Mangos: $8x_1 + 5x_2 \leq 40$
- Pears: $10x_2 \leq 50$

The objective function to maximize the net profit is:
- Maximize $5x_1 + 6.5x_2$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'mango-lovers packages'), ('x2', 'regular packages')],
    'objective_function': '5*x1 + 6.5*x2',
    'constraints': [
        '4*x1 + 3*x2 <= 30',
        '8*x1 + 5*x2 <= 40',
        '10*x2 <= 50',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobi

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

    # Define variables
    x1 = model.addVar(name="mango-lovers_packages", lb=0, vtype=gurobi.GRB.CONTINUOUS)
    x2 = model.addVar(name="regular_packages", lb=0, vtype=gurobi.GRB.CONTINUOUS)

    # Objective function
    model.setObjective(5 * x1 + 6.5 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(4 * x1 + 3 * x2 <= 30, name="lemons_constraint")
    model.addConstr(8 * x1 + 5 * x2 <= 40, name="mangos_constraint")
    model.addConstr(10 * x2 <= 50, name="pears_constraint")

    # Optimize model
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Mango-lovers packages: {x1.varValue}")
        print(f"Regular packages: {x2.varValue}")
        print(f"Max Profit: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```