## Step 1: Define the symbolic representation of the problem
Let's denote the number of small teddy bears as $x_1$ and the number of large teddy bears as $x_2$. The profit per small teddy bear is $50 and per large teddy bear is $8. The time required for filling a small teddy bear is 5 minutes and for a large teddy bear is 10 minutes, with 700 minutes available per day for filling. The time required for stitching a small teddy bear is 25 minutes and for a large teddy bear is 35 minutes, with 900 minutes available per day for stitching.

## Step 2: Formulate the objective function
The objective is to maximize profit. The profit per small teddy bear is $50, and per large teddy bear is $8. Therefore, the objective function can be written as: $50x_1 + 8x_2$.

## 3: Define the constraints
1. Filling time constraint: $5x_1 + 10x_2 \leq 700$
2. Stitching time constraint: $25x_1 + 35x_2 \leq 900$
3. Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

## 4: Symbolic representation in the required format
```json
{
'sym_variables': [('x1', 'small teddy bear'), ('x2', 'large teddy bear')],
'objective_function': '50*x1 + 8*x2',
'constraints': [
'5*x1 + 10*x2 <= 700',
'25*x1 + 35*x2 <= 900',
'x1 >= 0',
'x2 >= 0'
]
}
```

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

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

    # Define the variables
    x1 = model.addVar(vtype=gurobi.GRB.INTEGER, name="small_teddy_bear")
    x2 = model.addVar(vtype=gurobi.GRB.INTEGER, name="large_teddy_bear")

    # Objective function: maximize 50*x1 + 8*x2
    model.setObjective(50*x1 + 8*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(5*x1 + 10*x2 <= 700, name="filling_time_constraint")
    model.addConstr(25*x1 + 35*x2 <= 900, name="stitching_time_constraint")
    model.addConstr(x1 >= 0, name="non_negativity_constraint_x1")
    model.addConstr(x2 >= 0, name="non_negativity_constraint_x2")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Number of small teddy bears: {x1.varValue}")
        print(f"Number of large teddy bears: {x2.varValue}")
        print(f"Max profit: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_teddy_bear_problem()
```