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

The objective function is to maximize profit. The profit per lens sold is $200, and the profit per tripod sold is $150. So, the objective function can be written as:

Maximize: $200x_1 + 150x_2$

Now, let's consider the constraints:

1. The store can spend at most $10000 on camera equipment. Each lens costs $400, and each tripod costs $300. This constraint can be written as:
$400x_1 + 300x_2 \leq 10000$

2. At least 10 lenses but at most 25 are sold each month. This gives us two constraints:
$x_1 \geq 10$
$x_1 \leq 25$

3. The number of tripods sold is at most a third of the number of lenses sold:
$x_2 \leq \frac{1}{3}x_1$

Here's the symbolic representation in JSON format:
```json
{
    'sym_variables': [('x1', 'number of lenses'), ('x2', 'number of tripods')], 
    'objective_function': '200*x1 + 150*x2', 
    'constraints': ['400*x1 + 300*x2 <= 10000', 'x1 >= 10', 'x1 <= 25', 'x2 <= (1/3)*x1']
}
```

Now, let's implement this in Gurobi using Python:
```python
from gurobipy import *

# Create a new model
m = Model("Camera Store Optimization")

# Define the variables
x1 = m.addVar(name="lenses", lb=10, ub=25)
x2 = m.addVar(name="tripods")

# Set the objective function
m.setObjective(200*x1 + 150*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(400*x1 + 300*x2 <= 10000, name="budget")
m.addConstr(x2 <= (1/3)*x1, name="tripod_limit")

# Optimize the model
m.optimize()

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