To solve the optimization problem described, 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 given information.

Let's denote:
- $x_1$ as the number of acres for growing sunflowers,
- $x_2$ as the number of acres for growing roses.

The objective is to maximize profit. Given that the profit per acre of sunflowers is $200 and the profit per acre of roses is $375, the objective function can be represented as:
\[ \text{Maximize: } 200x_1 + 375x_2 \]

The constraints are based on the available land and the limit on plant nutrition. The florist has 40 acres in total, so:
\[ x_1 + x_2 \leq 40 \]

Additionally, sunflowers require 5 kg of plant nutrition per acre, and roses require 8 kg of plant nutrition per acre, with a total limit of 230 kg of plant nutrition. This gives us the constraint:
\[ 5x_1 + 8x_2 \leq 230 \]

Both $x_1$ and $x_2$ must be non-negative since they represent acres, which cannot be negative.

The symbolic representation in JSON format 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']
}
```

Now, let's write the Gurobi code to solve this linear programming problem:

```python
from gurobipy import *

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

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

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

# Add constraints
m.addConstr(x1 + x2 <= 40, "total_acres")
m.addConstr(5*x1 + 8*x2 <= 230, "plant_nutrition")

# Optimize the model
m.optimize()

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