## Step 1: Define the symbolic representation of the problem

Let's denote:
- $x_1$ as the number of Package 1 sold
- $x_2$ as the number of Package 2 sold

The objective is to maximize profit, where the profit per Package 1 is $10 and per Package 2 is $12. So, the objective function is:

Maximize: $10x_1 + 12x_2$

## Step 2: Define the constraints based on the given resources

The museum has 1000 souvenirs and 1400 snacks. Package 1 contains 5 souvenirs and 10 snacks, and Package 2 contains 4 souvenirs and 15 snacks. This gives us the following constraints:

- For souvenirs: $5x_1 + 4x_2 \leq 1000$
- For snacks: $10x_1 + 15x_2 \leq 1400$
- Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

## 3: Symbolic representation in JSON format

```json
{
    'sym_variables': [('x1', 'Package 1'), ('x2', 'Package 2')],
    'objective_function': '10*x1 + 12*x2',
    'constraints': [
        '5*x1 + 4*x2 <= 1000',
        '10*x1 + 15*x2 <= 1400',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## 4: Convert the problem into Gurobi code in Python

```python
import gurobi

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

    # Define variables
    x1 = model.addVar(name="Package_1", lb=0, vtype=gurobi.GRB.CONTINUOUS)
    x2 = model.addVar(name="Package_2", lb=0, vtype=gurobi.GRB.CONTINUOUS)

    # Objective function: Maximize 10*x1 + 12*x2
    model.setObjective(10*x1 + 12*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(5*x1 + 4*x2 <= 1000, name="souvenir_constraint")
    model.addConstr(10*x1 + 15*x2 <= 1400, name="snack_constraint")

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Number of Package 1 to sell: {x1.varValue}")
        print(f"Number of Package 2 to sell: {x2.varValue}")
        print(f"Max Profit: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_museum_gift_shop_problem()
```