To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints based on the problem statement.

Let's denote:
- \(x_1\) as the number of servings of chicken curry.
- \(x_2\) as the number of servings of goat curry.

The objective is to maximize profit. Given that the profit per serving of chicken curry is $5 and the profit per serving of goat curry is $7, the objective function can be written as:
\[ \text{Maximize:} \quad 5x_1 + 7x_2 \]

Now, let's consider the constraints based on the available resources:
- Tomatoes: \(1x_1 + 2x_2 \leq 20\)
- Curry paste: \(2x_1 + 3x_2 \leq 30\)
- Water: \(3x_1 + 1x_2 \leq 25\)

Additionally, the number of servings cannot be negative:
- \(x_1 \geq 0\)
- \(x_2 \geq 0\)

Thus, the symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'number of servings of chicken curry'), ('x2', 'number of servings of 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'
    ]
}
```

To solve this problem using Gurobi in Python, we can use the following code:
```python
from gurobipy import *

# Create a new model
m = Model("Curry_Optimization")

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

# Set the objective function
m.setObjective(5*x1 + 7*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 + 2*x2 <= 20, "tomatoes")
m.addConstr(2*x1 + 3*x2 <= 30, "curry_paste")
m.addConstr(3*x1 + x2 <= 25, "water")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print(f"Optimal solution: {x1.varName} = {x1.x}, {x2.varName} = {x2.x}")
    print(f"Maximum profit: {m.objVal}")
else:
    print("No optimal solution found")
```