## Step 1: Define the symbolic representation of the problem
Let's denote the amount invested in the film industry as $x_1$ and the amount invested in the healthcare industry as $x_2$. The objective is to maximize the return on investment, which is $0.08x_1 + 0.10x_2$. The constraints are:
- The total investment is $200,000: $x_1 + x_2 = 200,000$
- At least 25% of the investment is in the film industry: $x_1 \geq 0.25 \times 200,000$
- At most 60% of the investment is in the healthcare industry: $x_2 \leq 0.60 \times 200,000$
- Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

## 2: Convert the problem into a symbolic representation
The symbolic representation can be written as:
```json
{
'sym_variables': [('x1', 'film industry investment'), ('x2', 'healthcare industry investment')],
'objective_function': '0.08*x1 + 0.10*x2',
'constraints': [
'x1 + x2 = 200000',
'x1 >= 0.25 * 200000',
'x2 <= 0.60 * 200000',
'x1 >= 0',
'x2 >= 0'
]
}
```

## 3: Translate the symbolic representation into Gurobi code
Now, let's translate this into Gurobi code in Python:
```python
import gurobi

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

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

    # Objective function: maximize 0.08*x1 + 0.10*x2
    model.setObjective(0.08 * x1 + 0.10 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x1 + x2 == 200000, name="total_investment")
    model.addConstr(x1 >= 0.25 * 200000, name="min_film_investment")
    model.addConstr(x2 <= 0.60 * 200000, name="max_healthcare_investment")

    # Solve the model
    model.optimize()

    # Print the results
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal investment in film industry: ", x1.varValue)
        print("Optimal investment in healthcare industry: ", x2.varValue)
        print("Maximum return on investment: ", model.objVal)
    else:
        print("The model is infeasible")

solve_investment_problem()
```