## Step 1: Define the symbolic representation of the problem
Let's denote the number of containers of nuts as $x_1$ and the number of containers of candy as $x_2$. The profit per container of nuts is $5, and the profit per container of candy is $3. The time spent on weighing and packaging for each container of nuts and candy is given.

## Step 2: Formulate the objective function
The objective is to maximize profit. The profit from $x_1$ containers of nuts is $5x_1$, and the profit from $x_2$ containers of candy is $3x_2$. Therefore, the objective function to maximize is $5x_1 + 3x_2$.

## 3: Define the constraints
Each container of nuts takes 10 minutes of weighing, and each container of candy takes 5 minutes of weighing. The total available time for weighing is 1000 minutes. This gives the constraint $10x_1 + 5x_2 \leq 1000$.
Each container of nuts takes 5 minutes of packaging, and each container of candy takes 8 minutes of packaging. The total available time for packaging is 1500 minutes. This gives the constraint $5x_1 + 8x_2 \leq 1500$.
Also, $x_1 \geq 0$ and $x_2 \geq 0$ because the number of containers cannot be negative.

## 4: Symbolic representation in the required format
```json
{
'sym_variables': [('x1', 'containers of nuts'), ('x2', 'containers of candy')],
'objective_function': '5*x1 + 3*x2',
'constraints': ['10*x1 + 5*x2 <= 1000', '5*x1 + 8*x2 <= 1500', 'x1 >= 0', 'x2 >= 0']
}
```

## 5: Convert the problem into Gurobi code
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(lb=0, name="nuts")
    x2 = model.addVar(lb=0, name="candy")

    # Define the objective function
    model.setObjective(5 * x1 + 3 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(10 * x1 + 5 * x2 <= 1000, name="weighing_constraint")
    model.addConstr(5 * x1 + 8 * x2 <= 1500, name="packaging_constraint")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Containers of nuts: {x1.varValue}")
        print(f"Containers of candy: {x2.varValue}")
        print(f"Max Profit: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```