Here's the formulation of the linear program and the Gurobi code to solve it:

**Decision Variables:**

* `x`: Number of regular phone models produced.
* `y`: Number of premium phone models produced.

**Objective Function:**

Maximize profit: `200x + 500y`

**Constraints:**

* Assembly time constraint: `70x + 100y <= 6000`
* Software verification time constraint: `25x + 30y <= 4000`
* Non-negativity constraints: `x >= 0`, `y >= 0`


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

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

# Create decision variables
x = m.addVar(vtype=GRB.CONTINUOUS, name="regular_models")
y = m.addVar(vtype=GRB.CONTINUOUS, name="premium_models")

# Set objective function
m.setObjective(200*x + 500*y, GRB.MAXIMIZE)

# Add constraints
m.addConstr(70*x + 100*y <= 6000, "assembly_constraint")
m.addConstr(25*x + 30*y <= 4000, "software_constraint")
m.addConstr(x >= 0, "regular_nonnegativity")
m.addConstr(y >= 0, "premium_nonnegativity")

# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print(f"Optimal Solution Found:")
    print(f"Number of regular models: {x.x}")
    print(f"Number of premium models: {y.x}")
    print(f"Maximum profit: ${m.objVal}")
elif m.status == GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization terminated with status {m.status}")

```
