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

Let's denote the acres of sunflowers as $x_1$ and the acres of roses as $x_2$. The objective is to maximize profit, where the profit per acre of sunflowers is $200 and the profit per acre of roses is $375. Therefore, the objective function can be represented as $200x_1 + 375x_2$.

## Step 2: Identify the constraints

The florist has 40 acres to grow sunflowers and roses, which gives us the constraint $x_1 + x_2 \leq 40$. Additionally, the florist wants to use at most 230 kg of plant nutrition, with sunflowers requiring 5 kg per acre and roses requiring 8 kg per acre, leading to the constraint $5x_1 + 8x_2 \leq 230$. Also, $x_1 \geq 0$ and $x_2 \geq 0$ because the acres of sunflowers and roses cannot be negative.

## 3: Symbolic representation

The symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'acres of sunflowers'), ('x2', 'acres of roses')],
    'objective_function': '200*x1 + 375*x2',
    'constraints': [
        'x1 + x2 <= 40',
        '5*x1 + 8*x2 <= 230',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## 4: Gurobi code

Now, let's implement this problem using Gurobi in Python:
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(lb=0, name="sunflowers")
    x2 = model.addVar(lb=0, name="roses")

    # Set the objective function
    model.setObjective(200 * x1 + 375 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(x1 + x2 <= 40, name="acres_constraint")
    model.addConstr(5 * x1 + 8 * x2 <= 230, name="nutrition_constraint")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Acre of sunflowers: {x1.varValue}")
        print(f"Acre of roses: {x2.varValue}")
        print(f"Max Profit: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_florist_problem()
```