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

Let's denote the number of servings of chicken curry as $x_1$ and the number of servings of goat curry as $x_2$. The objective is to maximize profit, which is $5x_1 + 7x_2$. The constraints based on the available ingredients are:

- Tomatoes: $1x_1 + 2x_2 \leq 20$
- Curry paste: $2x_1 + 3x_2 \leq 30$
- Water: $3x_1 + 1x_2 \leq 25$
- Non-negativity: $x_1 \geq 0, x_2 \geq 0$

## Step 2: Express the symbolic representation in the required format

The symbolic variables are:
- $x_1$ for chicken curry
- $x_2$ for goat curry

The objective function is: $5x_1 + 7x_2$

The constraints are:
- $x_1 + 2x_2 \leq 20$
- $2x_1 + 3x_2 \leq 30$
- $3x_1 + x_2 \leq 25$
- $x_1 \geq 0$
- $x_2 \geq 0$

In the required format:
```json
{
    'sym_variables': [('x1', 'chicken curry'), ('x2', 'goat curry')],
    'objective_function': '5*x1 + 7*x2',
    'constraints': [
        'x1 + 2*x2 <= 20',
        '2*x1 + 3*x2 <= 30',
        '3*x1 + x2 <= 25',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Step 3: Convert the problem into Gurobi code

Now, let's implement this problem using Gurobi in Python:

```python
import gurobi

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

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

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

    # Define the constraints
    model.addConstr(x1 + 2 * x2 <= 20, name="tomatoes_constraint")
    model.addConstr(2 * x1 + 3 * x2 <= 30, name="curry_paste_constraint")
    model.addConstr(3 * x1 + x2 <= 25, name="water_constraint")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Chicken curry servings: {x1.varValue}")
        print(f"Goat curry servings: {x2.varValue}")
        print(f"Max profit: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```