To solve this optimization problem, we first need to convert the natural language description into a symbolic representation. Let's denote the number of oval rocks as $x_1$ and the number of rectangular rocks as $x_2$. 

The objective is to maximize profit. The profit per oval rock is $7, and the profit per rectangular rock is $9. Therefore, the objective function can be represented algebraically as:

Maximize: $7x_1 + 9x_2$

The constraints are based on the time available for washing and polishing. For washing, oval rocks require 10 minutes each, and rectangular rocks require 15 minutes each, with a total of 2000 minutes available. This can be represented as:

$10x_1 + 15x_2 \leq 2000$

For polishing, both types of rocks require 12 minutes each, with a total of 2500 minutes available. This constraint is:

$12x_1 + 12x_2 \leq 2500$

Additionally, the number of rocks cannot be negative, so we have non-negativity constraints:

$x_1 \geq 0$
$x_2 \geq 0$

Thus, the symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'number of oval rocks'), ('x2', 'number of rectangular rocks')],
    'objective_function': '7*x1 + 9*x2',
    'constraints': ['10*x1 + 15*x2 <= 2000', '12*x1 + 12*x2 <= 2500', '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("Rock_Collection")

# Add variables
x1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="oval_rocks")
x2 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="rectangular_rocks")

# Set the objective function
m.setObjective(7*x1 + 9*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(10*x1 + 15*x2 <= 2000, "washing_time")
m.addConstr(12*x1 + 12*x2 <= 2500, "polishing_time")

# Optimize the model
m.optimize()

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