Here's the formulation and the Gurobi code:

**Decision Variables:**

*  `s`: Number of sunflower bouquets sold.
*  `r`: Number of rose bouquets sold.

**Objective Function:**

Maximize profit: `7s + 12r`

**Constraints:**

* Clipping time: `4s + 5r <= 1200`
* Packaging time: `3s + 7r <= 800`
* Minimum sunflowers: `s >= 30`
* Non-negativity: `s >= 0`, `r >= 0`


```python
import gurobipy as gp
from gurobipy import GRB

# Create a new model
m = gp.Model("Toms_Florist")

# Create variables
s = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="sunflowers") # Number of sunflower bouquets
r = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="roses") # Number of rose bouquets

# Set objective function
m.setObjective(7*s + 12*r, GRB.MAXIMIZE)

# Add constraints
m.addConstr(4*s + 5*r <= 1200, "clipping_time")
m.addConstr(3*s + 7*r <= 800, "packaging_time")
m.addConstr(s >= 30, "min_sunflowers")


# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print(f"Optimal profit: ${m.objVal:.2f}")
    print(f"Number of sunflower bouquets: {s.x:.2f}")
    print(f"Number of rose bouquets: {r.x:.2f}")
elif m.status == GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization ended with status {m.status}")

```
