To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. Let's denote:

- $x_1$ as the number of mango-lovers packages,
- $x_2$ as the number of regular packages.

The objective function aims to maximize the net profit. Given that each mango-lovers package yields a profit of 5 euros and each regular package yields a profit of 6.5 euros, the objective function can be written as:

Maximize: $5x_1 + 6.5x_2$

The constraints are based on the availability of stock:

- For lemons: $4x_1 + 3x_2 \leq 30$ (since each mango-lovers package requires 4 lemons and each regular package requires 3 lemons, and there are 30 lemons available),
- For mangos: $8x_1 + 5x_2 \leq 40$ (since each mango-lovers package requires 8 mangos and each regular package requires 5 mangos, and there are 40 mangos available),
- For pears: $10x_2 \leq 50$ (since each regular package requires 10 pears and there are 50 pears available).

Additionally, the quantities of packages cannot be negative:

- $x_1 \geq 0$
- $x_2 \geq 0$

Thus, the symbolic representation of the problem is:

```json
{
    'sym_variables': [('x1', 'number of mango-lovers packages'), ('x2', 'number of 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'
    ]
}
```

To solve this problem using Gurobi in Python, we can use the following code:

```python
from gurobipy import *

# Create a new model
m = Model("Fruit Store Optimization")

# Define variables
x1 = m.addVar(lb=0, name="mango_lovers_packages")
x2 = m.addVar(lb=0, name="regular_packages")

# Set the objective function
m.setObjective(5*x1 + 6.5*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(4*x1 + 3*x2 <= 30, "lemon_constraint")
m.addConstr(8*x1 + 5*x2 <= 40, "mango_constraint")
m.addConstr(10*x2 <= 50, "pear_constraint")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Number of mango-lovers packages: {x1.x}")
    print(f"Number of regular packages: {x2.x}")
    print(f"Max Profit: {m.objVal}")
else:
    print("No optimal solution found")
```