## Step 1: Define the symbolic representation of the problem
Let's denote the number of beavers as $x_1$ and the number of bears as $x_2$. The objective is to maximize profit, where the profit per beaver is $5 and per bear is $7. The company has 2000 minutes available, with each beaver taking 10 minutes and each bear taking 15 minutes. Additionally, the company must make at least three times as many beavers as bears.

## Step 2: Formulate the objective function
The objective function to maximize profit is $5x_1 + 7x_2$.

## 3: Formulate the constraints
The constraints are:
1. $10x_1 + 15x_2 \leq 2000$ (time constraint)
2. $x_1 \geq 3x_2$ (beaver to bear ratio constraint)
3. $x_1 \geq 0$ and $x_2 \geq 0$ (non-negativity constraints, as the number of animals cannot be negative)

## 4: Symbolic representation in the required format
```json
{
'sym_variables': [('x1', 'beavers'), ('x2', 'bears')], 
'objective_function': '5*x1 + 7*x2', 
'constraints': ['10*x1 + 15*x2 <= 2000', 'x1 >= 3*x2', '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(name="beavers", lb=0, vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="bears", lb=0, vtype=gurobi.GRB.INTEGER)

    # Define the objective function
    model.setObjective(5 * x1 + 7 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(10 * x1 + 15 * x2 <= 2000, name="time_constraint")
    model.addConstr(x1 >= 3 * x2, name="beaver_to_bear_ratio")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution: {x1.varName} = {x1.x}, {x2.varName} = {x2.x}")
    else:
        print("The model is infeasible")

solve_optimization_problem()
```