## Step 1: Define the symbolic representation of the problem
Let's denote the acres of tulips as $x_1$ and the acres of daffodils as $x_2$. The objective is to maximize profit, which is $325x_1 + 200x_2$. The constraints are:
- $x_1 + x_2 \leq 200$ (total acres constraint)
- $10x_1 + 5x_2 \leq 1500$ (budget constraint)
- $x_1 \geq 0$ and $x_2 \geq 0$ (non-negativity constraints)

## Step 2: Convert the problem into a symbolic representation
The symbolic representation is:
```json
{
    'sym_variables': [('x1', 'acres of tulips'), ('x2', 'acres of daffodils')],
    'objective_function': '325*x1 + 200*x2',
    'constraints': [
        'x1 + x2 <= 200',
        '10*x1 + 5*x2 <= 1500',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Step 3: Translate the symbolic representation into Gurobi code
Now, let's translate this into Gurobi code in Python:

```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="tulips", lb=0, ub=200, obj=325)
    x2 = model.addVar(name="daffodils", lb=0, ub=200, obj=200)

    # Add constraints
    model.addConstr(x1 + x2 <= 200, name="total_acres")
    model.addConstr(10*x1 + 5*x2 <= 1500, name="budget")

    # Set the model to maximize the objective function
    model.setObjective(x1.obj * x1.varSize + x2.obj * x2.varSize, gurobi.GRB.MAXIMIZE)

    # Optimize the model
    model.optimize()

    # Print the results
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Acres of tulips: {x1.x}")
        print(f"Acres of daffodils: {x2.x}")
        print(f"Max profit: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```

The above Gurobi code defines the optimization problem with the correct objective function and constraints, and then solves it to find the optimal acres of tulips and daffodils that maximize profit. 

However, a more accurate way of implementing it is shown below:

```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="tulips", lb=0, ub=200, obj=325)
    x2 = model.addVar(name="daffodils", lb=0, ub=200, obj=200)

    # Add constraints
    model.addConstr(x1 + x2 <= 200, name="total_acres")
    model.addConstr(10*x1 + 5*x2 <= 1500, name="budget")

    # Optimize the model
    model.optimize()

    # Print the results
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Acres of tulips: {x1.x}")
        print(f"Acres of daffodils: {x2.x}")
        print(f"Max profit: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```